Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

The performance of mass testing strategies for COVID-19:

a case study for Costa Rica

Maikol Solís1*; http://orcid.org/0000-0002-8776-6142

Carlos Pasquier-Jaramillo1; https://orcid.org/0009-0004-8758-6872

Santiago Nuñez-Corrales2; http://orcid.org/0000-0003-4342-6223

Germán Madrigal-Redondo3; http://orcid.org/0000-0002-9856-4044

Andrés Gatica-Arias4; http://orcid.org/0000-0002-3841-0238

1. Centro de Investigación en Matemática Pura y Aplicada (CIMPA), Escuela de Matemática, Universidad de Costa Rica,

San José, Costa Rica; maikol.solis@ucr.ac.cr (*Correspondence); carlos.pasquier@ucr.ac.cr

2. National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, Urbana, Illinois, United

States of America; nunezco2@illinois.edu

3. Instituto de Investigaciones Farmacéuticas (INIFAR), Universidad de Costa Rica, San José, Costa Rica;

german.madrigal@ucr.ac.cr

4. Escuela de Biología, Universidad de Costa Rica, San José, Costa Rica; andres.gatica@ucr.ac.cr

Received 09-IV-2024. Corrected 28-XI-2024. Accepted 04-III-2025.

ABSTRACT

Introduction: In this article, we derive the behavior of four different mass testing strategies, grounded in guide-

lines and public health policies issued by the Costa Rican public healthcare system.

Objective: To formally develop the changes of each studied mass testing strategy under different contexts related

to people’s risk, costs of testing, and accessibility to alternative testing technologies.

Methods: We take over a pre-classifier applied to individuals capable of partitioning suspected individuals into

low-risk and high-risk groups. We consider the impact of three testing technologies: RT-qPCR, antigen-based

testing, and saliva-based testing (RT-LAMP). When available, we introduced a category of essential workers.

Results: Numerical simulation results confirm that strategies using only RT-qPCR tests cannot achieve sufficient

stock capacity to provide efficient detection regardless of prevalence, sensitivity, or specificity. Strategies that har-

ness the power of pooling and RT-LAMP either maximize stock capacity, detection efficiency, or both.

Conclusions: Investing in data quality and classification accuracy can improve the odds of achieving pandemic

control and mitigation. Future work will be focused on, based on our findings, constructing representative syn-

thetic data through agent-based modeling and studying the properties of specific pre-classifiers under various

scenarios.

Keywords: mass testing; COVID-19 Costa Rica; RT-qPCR; antigen test; RT-LAMP; pooling; detection strategies.

RESUMEN

Desempeño de estrategias de pruebas masivas para COVID-19: un estudio de caso para Costa Rica

Introducción: En este artículo, derivamos el comportamiento de cuatro diferentes estrategias de pruebas masi-

vas, basadas en las directrices y políticas de salud pública emitidas por el sistema de salud pública de Costa Rica.

Objetivos: Desarrollar formalmente los cambios de cada estrategia de pruebas masivas estudiada bajo diferentes

contextos relacionados con riesgo de las personas, costos de la prueba y acceso a tecnologías alternativas de

pruebas.

https://doi.org/10.15517/rev.biol.trop..v73i1.57971

BIOMEDICINE

2Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

INTRODUCTION

The recent SARS-CoV-2 pandemic has

made mass testing strategies a key tool for

managing and understanding the trajectory of

communicable diseases. Recent studies sug-

gest these strategies help to control outbreaks

if they are underpinned by robust estimations

of the pandemic’s current and future impact.

This requires a framework rooted in evidence-

based planning, steering clear of potentially

misleading metrics, and using complementary,

information-driven efforts (Grantz et al., 2021).

Implementing effective testing and epidemi-

ological surveillance is hampered by many

obstacles in countries around the globe, rang-

ing from structural deficiencies in public health

systems (Caliendo et al., 2013) to financial

constraints that restrict access to essential tech-

nologies (Beaudevin et al., 2021; Yang & Roth-

man, 2004).

Let us first define asymptomatic as the

population infected that will never develop

symptoms, while pre-symptomatic patients

develop symptoms after the incubation time

(He et al., 2020; Tindale et al., 2020). Maximiz-

ing resources for a mass testing strategy result

in a nonlinear allocation issue with generalized

cost functions (Brandeau, 2004). However, the

overall problem can change when we introduce

pre-symptomatic and asymptomatic patients

because of their potential to spread the virus

unnoticed. The work by Kırkızlar et al. (2010)

found, for the asymptomatic case, the cost-

effectiveness of mass testing intervention is

equivalent to a Markov Decision Process. This

process must include prior data about indi-

vidual test outcomes and behavioral change

induced by an awareness of the disease. Clinical

and theoretical studies reveal the importance of

controlling the pre-symptomatic and asymp-

tomatic individuals in early stages. Some exam-

ples include mass testing with pooling samples

(Comess et al., 2022), comparative analysis

with isolation components (Du et al., 2021),

simulations with rapid saliva-based testing

(Núñez-Corrales & Jakobsson, 2020). Longitu-

dinal studies revealed a conservative estimate

of 30-45 % asymptomatic cases, including pre-

symptomatic (Oran & Topol, 2020; Oran &

Topol 2021; Sah et al., 2021).

One of the major flaws in every mass

testing strategy is the availability of resources

and technology to perform it. Since December

2019, the healthcare systems in Latin America

struggled with inadequate and tracking systems

mostly due to the infeasibility to perform RT-

qPCR to the entire population (Rubinstein,

2025). During high peak waves, global scarcity

of reagents forces the laboratories to use alter-

native options (Avaniss-Aghajani et al., 2020).

Despite its accuracy, RT-qPCR was an inad-

equate solution, given that it requires robust

laboratory facilities and trained staff, both of

Métodos: Asumimos un pre-clasificador aplicado a individuos, capaz de dividir a los sospechosos en grupos de

bajo riesgo y alto riesgo. Consideramos el impacto de tres tecnologías de prueba: RT-qPCR, pruebas basadas en

antígenos y pruebas de saliva (RT-LAMP). Cuando estuvo disponible, introdujimos una categoría de trabajadores

esenciales.

Resultados: Los resultados de simulaciones numéricas confirman que las estrategias que utilizan únicamente

pruebas RT-qPCR no pueden lograr una capacidad de existencias suficiente para proporcionar una detección

eficiente, independientemente de la prevalencia, sensibilidad o especificidad. Las estrategias que aprovechan el

poder tanto del agrupamiento (pooling) como del RT-LAMP maximizan la capacidad de existencias o la eficiencia

de detección, o ambos.

Conclusiones: Invertir en la calidad de los datos como en la precisión de la clasificación puede mejorar las pro-

babilidades de lograr el control y la mitigación de la pandemia. El trabajo futuro se concentrará, basándonos en

nuestros hallazgos, en construir datos sintéticos representativos a través de modelado basado en agentes y estudiar

las propiedades de pre-clasificadores específicos bajo varios escenarios.

Palabras clave: pruebas masivas; COVID-19 Costa Rica; RT-qPCR; pruebas de antígenos; RT-LAMP; agrupa-

miento; estrategias de detección.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

which are limited. Reporting of results normal-

ly takes 2-5 days, depending on the healthcare

system capacity to process samples and perform

administrative follow-up (Watkins et al., 2021).

The cost per test ranges between $ 50 to $ 100

per result (Centers for Medicare & Medicaid

Services, 2020).

Antigen-based tests comprise a less expen-

sive alternative strategy to RT-qPCR. Those

could cost between $ 30 to $ 50 and can

give a result within a maximum of two hours

(Wiencek et al., 2020). Detection becomes reli-

able during the first week after symptoms onset

(Mercer & Salit, 2021), and in vitro studies

show high specificity (> 99 %) but low sensitiv-

ity (> 66 % for nucleocapsid, > 85 % spike). In

practice, the proportion of false negatives can

increase to unacceptable levels when testing

occurs after the week in which first symptoms

appear. To provide a baseline for antigen-based

alternatives, World Human Organization estab-

lished in 2020 a minimum sensitivity of 80

% and specificity of 97 % compared with RT-

qPCR. The Center for Disease Control and

Prevention (CDC) published a set of guidelines

and good practices along these lines (CDC,

2020), including confirmatory RT-qPCR test

when antigen-based alternatives yield incon-

clusive results. Another technology is the

Reverse Transcriptase Loop Mediated Isother-

mal Amplification (RT-LAMP). Its technology

is similar to RT-qPCR given their molecular

detection (Österdahl et al., 2020). The protocol

can be scaled up because of the use of abundant

standard reagents (Saidani et al., 2021) and,

when tests are inconclusive, these are repeated

at low costs. In relation to the progression of the

disease, preliminary research on the sensitivity

of RT-LAMP found its value on 85.2 % during

the first nine days of infection and 44.4 % after-

ward, and an average of 60 % for asymptomatic

patients (Nagura-Ikeda et al., 2020).

The mentioned technologies can be

improved with alternative strategies like pool-

ing or retesting. The pooling technique was

first proposed by Dorfman (1943). The scheme

divides the total number into different pools

and tests each group. The negative groups

declare all the individuals as negative. With

the positive ones, another round of individual

testing allows the detection of the infected

individual(s). Performing multiple tests on the

low-risk population constitutes an alternative

to pooling. This scheme implies weekly or

biweekly tests for the same group of individu-

als. Several studies have shown that frequent

testing reduces the positivity rate and the num-

ber of sick leaves among workers (Haigh &

Gandhi, 2021; Larremore et al., 2021; Plantes et

al., 2021; Sandmann et al., 2020).

The review by Prado et al., (2023) about

the pandemic situation showed how the RT-

qPCR was the primary line of detection during

the early phase of the pandemic coupled with

contact tracing. In 2020, the lack and efficient

diagnostic screening system, the country suf-

fered multiple delays and high costs by tourists

and commercial transporters (García-Puerta

et al., 2023). In 2021, the Costa Rican Minis-

try of Health (Ministerio de Salud Costa Rica

[MINSA]) introduced regulations for antigen-

based testing (MINSA, 2021b). The regulation

allowed private healthcare providers to test with

this technology. A negative antigen-based test

performed by these private providers does not

require an RT-qPCR confirmatory test, though.

This assumption suggests that negative patients

could still be healthy, but a negative test result

may not completely rule out the possibility of

infection due to the test’s low sensitivity. Only

much later in their opening, the authorities

allowed commercial import of antigen-based

tests for the public. Hence, the antigen-based

testing was not part of a mass testing strategy

in Costa Rica.

This article explores the Costa Rica case

on limited testing capabilities with RT-qPCR,

scarce testing alternatives, and limited infor-

mation infrastructure for patient tracing. We

examine strategies for maximizing infrastruc-

ture effectiveness using multiple test types. The

study shows in silico the behavior of different

population-level strategies under the existence

of mechanisms capable of predicting individual

risk of contagion. We hypothesize the popula-

tion’s infection stays on a prior set of individual

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and collective factors that allow predicting the

outcome. Therefore, given any available-and

possibly anonymized-information, the authori-

ties allocate testing during an emergency.

To this end, we split the population into

low and high risk. The high-risk group con-

tains all symptomatic individuals, their epi-

demiological nexus, healthcare workers and

essential workers with frequent viral expo-

sure. The low-risk group consists of individuals

whose features prevent COVID-19 like lack

of comorbidities, young age, accessibility to

health, among others (Zhang et al., 2023). It

also represents the largest potential gain for

proactive screening of pre-and asymptomatic

populations. The work by Escobar et al. (2022)

applied a Gradient Boosting Machine (GBM)

to clinical and sociodemographic factors to

split the population into those groups. They

then evaluated pooled testing by computing

the efficiency between Dorfman’s pooling and

matrix pooling strategies, as well as one-stage

and two-stage strategies. Reported efficiency

gains were significant.

In this work, we study the statistical and

mathematical mechanisms behind massive test-

ing strategies when using a classifier to detect

subjects at risk before testing. We explore four

different strategies. Strategy 1 follows the offi-

cial guidelines for the high-risk group, ignoring

the low-risk one. For the low-risk group, Strat-

egy 2 uses a pooling technique, while Strategy 3

uses a multiple testing scheme. Finally, Strategy

4 combines RT-qPCR, Antigen and RT-LAMP

to maximize the benefits of each technology.

We formulated a probabilistic model to quan-

tify the costs, detected positives and number

of tests per person required in each strategy.

To the best of our knowledge, the Costa Rican

authorities have not implemented a similar

study. Understanding the statistical properties

of testing strategies based on separating vulner-

able individuals into risk categories can aid in

optimizing the efficiency of existing resources

for future pandemics.

MATERIAL AND METHODS

In this study, we performed an in silico

evaluation of the behavior of different massive

testing strategies preceded by a patient classifi-

cation mechanism. This section describes the

contextual framework of our work.

Sensitivity, specificity, PPV and NPV: Let

DP be the condition of having the disease (i.e.,

infected) and DN the condition of being not

infected. The prevalence is estimated by P(DP)

such that P(DN) = 1 - P(DP). Let also N be the

total population undergo testing. Thus, N x

P(DP) are the true infected and N x (1 - P(DP))

the true healthy people. Denote as RPj and RNj

the results positive and negative of each test,

respectively. In addition, let j = PCR, Ag or

LAMP denote each available testing technology,

RT-qPCR, Antigen or RT-LAMP respectively.

We can thus define sensitivity as the proportion

of people infected who are correctly identified

as positive in the test, or P(RPj | DP). Specific-

ity constitutes the proportion of people not

infected who are correctly identified as negative

in the test, or P(RNj | DN). When the prevalence

is known, the relationships for testing positive

or negative in a test become,

P(RPj) = P(RPj | DP)P(DP) + (1 - P(RNj | DN))(1-P(DP))P(RNj) = 1 - P(RPj).

Meanwhile, the positive predictive value (PPV) is the probability of being actually positive

when infected, or P(DP | RPj). In contrast, the negative predicted value (NPV) is the probability of

being negative while not having the disease, or P(DN | RNj). By virtue of Bayes’ theorem,

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

In general, sensitivity and specificity are

fixed values given testing technology. Never-

theless, NPV and PPV depend on the current

prevalence. More generally, PPV increases and

NPV decreases as a function of increasing

prevalence. Given RT-qPCR testing has high

sensitivity and specificity, PPV and NPV val-

ues are normally above 90 % regardless of

the prevalence.

Antigen-based testing requires special

attention due to its low sensitivity (80 %). For

a population with 25-50 % prevalence, antigen-

based testing will yield a PPV of 90-96 % given

that the test is used 5 days after symptom onset.

Increasing prevalence, for example, above 36 %

yields a decrease in NPV decreases below 90 %,

producing many false negatives. In other words,

more than 10 % of patients tested were declared

as negative when in reality they were infected.

The procedure to follow in this case is to collect

another sample across individuals whose results

were negative results and perform an RT-qPCR

confirmatory test.

Prevalence in the range of 1-10 % entails

low impact from false negatives (NPV = 98-100

%). However, an unacceptable number of

false positives arises when PPV reaches values

between 21-75 %. That is, large quantities of

healthy people are being declared as infected

when they are not, with potentially negative

impacts to workforce availability. This can

become particularly significant when essential

workers are involved. The recommended strat-

egy for this group therefore becomes to apply

antigen-based testing with high sensitivity to all

the population for an initial screening (CDC,

2020). And to either perform a second round

of antigen-based testing (with higher specific-

ity) or an RT-qPCR test, those whose first test

was positive.

Mass testing strategies: Pooling and mul-

tiple testing: Increasing the effectiveness of

mass testing can be achieved through pooling

or multiple testing. Pool testing requires three

important conditions to work: (a) if all mem-

bers in a group are negative, then the group

yields negative in the pool analysis; (b) a single

positive sample within a group makes the group

test result positive-further testing is necessary

to identify the true positives-and (c) the frac-

tion of expected positive cases is small. Current

literature describes two large classes of pooling

strategies: adaptive and non-adaptive. Adap-

tive ones require incremental results to further

stratify testing across the population. Non-

adaptive methods set the pooling scheme prior

to testing, and each group is tested independent

of each other (Millioni & Mortarino, 2021). To

simplify the estimation process, we used the

most commf7on algorithm pooling strategy,

the one-dimensional (1D) protocol (Dorfman,

1943). This scheme consists of mixing a group

of samples, taken in batches. The analysis is

then carried out only over these batches. If one

batch is positive, then all members must be

analyzed individually.

The main limitation with this technique

is that it becomes useful only at low preva-

lence levels. Consider, for example, 100 peo-

ple divided into groups of ten with only one

positive patient (prevalence of 1 %). In this

situation, nine of ten groups will be assigned a

negative result. The remainder group with one

positive case should be tested entirely again.

The strategy described above required 20 tests

instead of 100. In contrast, if ten people are

infected and each group has one positive case

in each group (prevalence of 10 %), this pool-

ing strategy results in a total of 110 tests. Other

issues include loss of sensitivity due to dilution

or possible artifacts introduced by the actual

sample collection protocol (Peeling et al., 2021;

Watkins et al., 2021).

When pooling schemes are infeasible, mul-

tiple testing provides a straightforward solu-

tion. Results in Du et al. (2021) show that

weekly testing and 2-week periods of isolation

work best when transmission rates are high. If

transmission rates decrease, then monthly test-

ing and 1-week isolation periods provide the

best solution to maintain the economy afloat.

Two unpredictable factors make it challeng-

ing to translate results into policy. First, the

asymptomatic and pre-symptomatic fractions

of the population tend to be the most uncertain,

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especially when testing strategies are being

devised. Knowing how they behave explains

the rate of disease spreading. Second, local

transmission rates are modulated by multiple

factors, including population, density, mitiga-

tion policies, and local immunity, where little

or no control is possible.

Costa Rican testing guidelines: In the

Costa Rican case, the Ministry of Health in

MINSA (2021b), MINSA (2022), defined

guidelines for antigen-based testing as an alter-

native to RT-qPCR, depending on whether

the patient is tested in public or private health

services. The discriminating element is the use

of RT-qPCR confirmatory test after an antigen-

based test outcome is negative within the public

healthcare system. The private system is exclud-

ed from required confirmatory testing. These

guidelines define a suspicious patient (i.e., high

risk) when both symptoms (e.g., high fever,

cold, loss of sense of smell sense) and a well

identified epidemiological nexus (e.g., living

with positive individuals, recent travel history)

are present. Asymptomatic patients are deemed

low risk. Therefore, the underlying principle

establishes that high-risk patients must go to

the public healthcare system, while the low-risk

ones are directed to the private one.

Costa Rican guidelines directly follow

CDC recommendations, which distinguish

between congregate and community living set-

tings. We note that Costa Rican guidelines

have failed to consider prevalence across the

population as a significant factor in how they

differentiate between public and private health

services. The main assumption behind this is

that every patient tested in the private service

has a low-risk of infection, and that conse-

quently, antigen-based testing is reliable. This

may not hold in the complex reality of disease

spread of a small size, emerging economy.

High and low risk classification: Any suc-

cessful mass testing strategy should be able to

screen rapidly individuals while controlling as

strictly as possible for false negatives and posi-

tives. Three elements are reported in this work

to achieve this goal: cost-effectiveness, positive

rate and number of tests per person. We focus

on the sequence of events leading to a confir-

matory test, depending on whether the person

is symptomatic or not and the current level of

prevalence of the disease. We hence propose a

set of alternative configurations informed by

features of the public-private healthcare system

discussed above. Our work includes a two-step

strategy for massive testing: classifying the

patients into high-risk and low-risk categories,

and later applying a suitable adaptive mass test-

ing strategy per group.

The general strategy proceeds as follows:

1. Collect or access patient data in advance

corresponding to factors that determine

the probability of becoming exposed to

COVID-19. Due to privacy reasons or

local legislation, the patient data could be

confidential. In those cases, we can use

aggregated statistics and estimate synthetic

models to simulate a usable data table.

2. Predict patient risk categories based using

data above. All symptomatic patients

or those with an epidemiological nexus

are automatically classified as high-risk

regardless of prediction outcomes.

3. Select a strategy based on the predicted

risk category:

a. High-risk group: provide antigen-

based testing if symptoms started for

5 days or less or provide RT-qPCR tes-

ting otherwise. All negative outcomes

must be confirmed with RT-qPCR.

b. Low-risk group:

i. Use a pooling technique with a

pool size of five.

ii. Perform antigen-based testing

across all groups and perform

antigen-based confirmatory tes-

ting to all members of groups

with at least one positive.

The effectiveness of each strategy will

depend on the prevalence, sensitivity, speci-

ficity, PPV and NPV of each test, as well as

on the accuracy of the predictive model. As

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mentioned before, we explore only the theo-

retical properties of such strategies assuming

an arbitrary predictive model. Models can be

fitted using a wide variety of information (i.e.,

residence-work location, socioeconomic status,

comorbidities, recent travel). Then, we will use

the combined characteristics of RT-qPCR and

Antigen tests to create a massive strategy for all

the population.

For the purposes of this study, define

CPCR = $100 and CAg = $50 as the cost of a single

RT-qPCR and antigen-based test, respectively.

Administrative expenses, fees and other costs

were excluded. We assume a total population of

N = 1000 individuals. For instance, using RT-

qPCR test for the entire population yields N ×

CPCR = $100 000.

We denote a high-risk classification out-

come by MH, and a low-risk one by ML. We

define the classifier’s sensitivity as P (MH | RPj),

which contrasts the prediction against labora-

tory test results for each testing technology j.

This value estimates the proportion of people

being classified as high-risk when they have

indeed a positive test result. To simplify, we

assume the same sensitivity for RT-qPCR and

antigen-based tests. We establish the following:

P (MH | RP) = P (MH | RPPCR) = P (MH | RPAg)

Meanwhile, the specificity P (ML | RNj) cor-

responds to the proportion of people classified

as low-risk having a negative result. Again, we

assume that both technologies have the same

specificity, and we denoted just as P (ML | RN).

For the purpose of our computational study,

we explored classifier combinations of sensitiv-

ity and specificity at 30 %, 60 % and 90 % for

both variables. Knowing the prevalence, we

can estimate

the probabilities of being classified as high or

low risk depending on the testing technology.

To combine both probabilities, we use the logit

transformation

which leads to

The corresponding values for PPV and NPV

are P (RPj | MH) and P (RNj | ML). By applying

Equation (1), this becomes on:

We obtain combined probabilities

P (RP | MH) and P (RN | ML) via a similar treat-

ment with the logit transformation.

In the context of antigen-based testing,

we denote as S-5 the event if a patient has less

than 5 days since the beginning of symptoms

and S+5 otherwise. Since neither the high-risk

condition nor the result of the test alter the

distribution of patient symptoms, we assume

that S-5 and S+5 are independent of MH, RPj or

RNj. While this assumption may not hold in all

the cases, it will not affect the results due to the

theoretical nature of this study. To re-estimate

the probability correctly when the assump-

tion does not hold, a detailed study of the

patients should make results more precise to

confirm the hypothesis. In our computational

experiments, we set P (S+5) with values of 30,

60 and 90 %. We define P (S-5) = 1 - P (S+5). We

assume a greater proportion of RT-qPCR tests

used directly on high-risk patients when (S+5)

increases, and the number of antigen-based

tests used at the group level increases when

(S-5) increases. The following sections define

formulas for the overall cost, number of tests

per person and number of positive reports of

each strategy.

Strategy 1: antigen-based testing: We

model this scenario based on the Costa Rican

public healthcare guidelines. Fig. 1 depicts the

steps involved in this strategy, which adds a

new decision layer prior to laboratory testing

(blue box). The layer uses a classifier to deter-

mine high-risk (red box) or low-risk (green

box) individuals. Using this label, the strategy

applies a different mechanism to each group.

The assumptions for this scenario are:

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1. Patients in the low-risk group (ML) are not

tested.

2. Patients in the high-risk group (MH) are

tested according to symptom onset: 1.

Patients with less than 5 days since the

beginning of symptoms (S-5) undergo anti-

gen-based testing.

a. If the test is positive (RPAg | MH), the

patient is declared positive. 2. If the

test is negative (RNAg | MH), apply a

confirmatory RT-qPCR (CPCR) test.

b. Apply an RT-qPCR test for patients

with more than 5 days after symptom

onset (S+5).

Fig. 1. Strategy 1, mass testing with RT-qPCR and antigen-based technologies.

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

To estimate overall costs, given by the

number of tests per person and positive cap-

tured by the strategy, we define formally each

component. First, the population at risk is

given by

NMH = N×P(MH)

Since we only apply tests to high-risk

patients, we can establish the number of tests

applied for each technology,

T1Ag = N(MH) × P(S-5) (2)

T1PCR = N(MH) × (P (S+5) + P (S+5)P (RNAg V MH) (3)

and then, the cost for this strategy becomes

C1 = CAg T1Ag + CPCR T1PCR

For the number of tests per person, we

simply compute

For the number of positive cases reported,

we estimate the population which has under-

gone either antigen-based or RT-qPCR testing

and multiply it by the probability of having a

positive result in each case. This estimate is

P1Reported = NMH P (S-5) + P (RPAg | MH)

+ P (RNAg | MH) P (RPPCR | MH) +

NMH P (S+5) P (RPPCR | MH)

Strategy 2: pooling: We maintain all fea-

tures from Strategy 1 but include a pooling

component for the low-risk group (SMF1). The

assumptions are

1. The high-risk group follows Strategy 1

2. Pooling is applied to the low-risk group

(ML), with a pool size of 5 samples.

Similarly, as before, we define the high-

risk group as NM and the low-risk one as

NL = N × P (ML).

Since we did not modify the number of

antigen-based tests, we use the same value

T1Ag as in Equation (2). The RT-qPCR tests

applied in this scenario disaggregate into two

components. The first one is the same in Equa-

tion (3) called here T1PCR. For the second one,

we need to determine the number of tests used

in the pooling strategy.

The first element to establish is the preva-

lence among the low-risk subpopulation. Given

the model, we need to estimate those individu-

als who are expected to be positive given the

ML classification. The negative predictive value

of the model is given by P (RN | ML). Therefore,

we define the prevalence in this subgroup as

the false omission rate estimated by pL = 1- P

(RN | ML).

If no loss of sensitivity occurs in the pool-

ing technique and that the sensitivity of an

RT-qPCR test is P (RPPCR | DP), we estimate the

number of positive groups

P2groups = [1 - (1 - P (RPPCR | DP) pL)g] NML

with given a total test population of size NML

divided into groups of size g. The total number

of tests required are

Having those elements, we define the total

number of RT-qPCR tests applied as

T2PCR = T1PCR + T2Pooling

and the total cost is, therefore,

C2 = CAg T2Ag + CPCR T2PCR

The estimate of the number of tests per

person required becomes

For the number of positive cases reported,

we have again two components. First, we have

the same number as Strategy 1 for the high-risk

population. For the low-risk branch, we need

to consider only those groups with positive

test outcomes. We estimate the probability that

their individual test in the Dorfman scheme

attains a positive result. We therefore multiply

the prediction outcome for pooling by the

10 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

positive predictive value of an RT-qPCR test

and by the group size,

P2Reported = P1Reported + gP2Pooling P(RPPCR | ML)

Strategy 3: consecutive antigen-based

testing: Another alternative to increase the

efficiency of Strategy 1 entails applying consec-

utive tests to the low-risk population (SMF2).

This requires applying an antigen-based test to

all low-risk patients, and in case of a positive

result, a second confirmatory test should be

performed within the next week or two. The

measure is suboptimal due to false positive rates

in current antigen-based testing technologies.

The assumptions behind this strategy are:

1. All patients in the high-risk group follows

Strategy 1.

2. All patients in the low-risk group (ML)

undergo antigen-based testing.

a. If the result is negative, we declare the

person negative.

b. If the result is positive, we apply a con-

firmatory antigen-based test within

one or two weeks.

The number of antigen-based tests has

two components, due to re-testing. For the

high-risk population, we use the same value as

Strategy 1, T1Ag. For the low-risk population, all

patients undergo a first round of testing, and

positive patients undergo a second one. At the

end, the total number of antigen-based tests

required during re-testing is

T3Retest = NML (1 + P(RPAg | ML))

and the total number of antigen-based tests

becomes

T3Ag = T1Ag + T3Retest

RT-qPCR tests applied are the same as the

Scenario 1, T1PCR. The total cost due to testing

for Strategy 3 is

C3 = CAg T3Ag + CPCR T1PCR.

For the number of tests per person, we

simply estimate

Finally, the number of positive cases

reported divides into two components. First,

we have the same number of positive cases as

Strategy 1 for the high-risk population. For the

low-risk branch, we need to consider only the

tests that were positives in the first or second

round. This estimate is defined as

P3Reported = P1Reported + ML P(RPAg | NL)2.

Strategy 4: the role of saliva-based test-

ing: Prior strategies model the current state of

healthcare guidelines in Costa Rica, anchored

in RT-qPCR tests as the main line of defense,

which does not scale for mass testing purposes.

Antigen-based testing has lower costs, but its

low sensitivity makes confirmatory tests of

negative results still necessary. An alternative

solution is to include saliva-based RT-LAMP

testing into the mix, as suggested by a prior

study (Segura-Ulate et al., 2022). RT-LAMP

and other saliva-based testing technologies

reach values above 90 % for sensitivity and

above 95 % for specificity and can be adapted

quickly to new variants. In addition, the sam-

pling process is inexpensive, requires lower

biosafety standards and trained personnel than

nasopharyngeal swabs.

The fourth strategy proposed here seeks

to overcome the flaws of other technologies by

targeting them to appropriate groups based on

a data-driven assessment of individual patient

risk. We first separate high-risk patients further

into essential workers and other high-risk. For

essential workers, an RT-qPCR test is mandato-

ry to ensure continuity of services without risk-

ing high numbers of false positives or negatives.

Other high-risk patients undergo saliva-based

RT-LAMP testing, well suited to in particular

for high peak waves and massive screening.

To capture all positive cases, a confirmatory

RT-LAMP should be performed over negative

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

cases. Finally, the low-risk group is subjected

to antigen-based testing at home or in point-

of-care (POC) centers. As with Strategy 3, all

positive cases must confirm their result with a

second test within one or two weeks.

The main assumptions behind this strategy

are:

1. Essential workers are tested with RT-qPCR.

2. Patients in the high-risk group are tested

with RT-LAMP.

a. If the result is positive, we declare the

person as positive.

b. If the result is negative, we perform a

confirmatory test by RT-LAMP.

3. Patients in the low-risk group () are tested

with antigen-based tests.

a. If the result is negative, we declare the

person as negative.

b. If the result is positive, we apply a con-

firmatory antigen-based test in one or

two weeks.

Where ME represents the class of essen-

tial workers on this Strategy. For simulation

purposes, we set the proportion of essential

workers at a fixed value of 1 %. The value is a

conservative estimate based on the 1.25 % of

total healthcare workers in Costa Rica: 2 470

in the Ministry of Health, 62 814 in the public

social security from a total population of 5 213

374 inhabitants (Brenes-Camacho et al., 2013;

Caja Costarricense de Seguro Social [CCSS],

2021; MINSA, 2021a). Therefore, we estimate

the high- and low-risk groups with the remain-

der of the population,

NMH = (N-NME) × P(MH)NML = (N-NME) × P(ML)

The number of tests applied in each case

will depend on the technology. For RT-qPCR,

we have

T4PCR = NME

RT-LAMP tests only apply to the high-risk

group, with a confirmatory test in case of a

negative result,

T4LAMP = NMH + NMH P(RN | MH).

For antigen-based tests, the number is

equal to that in Strategy 3, . The strategy total

costs become

C4 = CPCR T4PCR + CLAMP T4LAMP + CAg T4Ag

and for the number of tests per person, we

estimate

Finally, the number of positive cases can be

decomposed into

P4Report = NME P(RPPCR) + NMH P(RPLAMP | MH)

(1 + P(RNLAMP | MH)) + NML P(RPAg | ML)2

RESULTS

In this section, we compare the strategies

above according to their costs (Ci), number

of tests per person (Tiperperson) and number of

positive cases reported (PiReported) for i = 1, 2, 3.

The total population used is N = 1 000 and the

prevalence ranges from 0 to 30 %. The cost of

an RT-qPCR test is set to $ 100 and an antigen-

based test to $ 50. Across all figures, the red

dashed line is the cost of applying an RT-qPCR

test to each true infected. Formally, it is equal to

1 000 × $ 100 × P(DP). Those reported as posi-

tive correspond to the number of true infected

individuals $ 100 × P(DP). For the number

of tests per person, we set to the constant 1

indicating a baseline. Blue lines represent the

percent of antigen-based tests used in each

strategy according to the proportion of people

showing symptoms for less than 5 days. From

dark to light blue, we assume proportions of

12 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

25, 50 and 75 %. The primary x axis represents

percent prevalence and the y axis varies per

target: cost in dollars number of people or tests

per person. Secondary axes show the model

specificity and sensitivity used in each case.

Our code is available in a GitHub repository for

reproducibility purposes.

Costs: Computational experiments show

that using the pre-classifier reduces the total

cost by correctly identifying the high-risk indi-

viduals in Strategy 1 (SMG1). As the pre-

classifier increases its predictive accuracy,

cost decreases to only for those truly infected.

Notice that specificity has a greater effect in

reducing cost relative to sensitivity. Since this

strategy excluded low-risk individuals, false

negatives do not contribute to the overall cost.

Conversely, false positive cases appear (i.e.,

false high-risk individuals), the strategy applies

an antigen-based test with a confirmatory RT-

qPCR in case of negative outcome.

Sensitivity and specificity modulate the

effectiveness of the classifier to rebalance the

overall cost structure depending on preva-

lence. Specificity determines the sign of the

slope of the resulting curves, while sensitiv-

ity determines the percentage of antigen-based

tests applied to the population. Proportion-

ally, applying more antigen-based tests becomes

more effective at prevalence values higher than

10 % with tests having high specificity (90 %)

and medium to high sensitivity (60 %, 90 %).

For Strategy 2, false positive cases repre-

sent the largest cost factor (SMG2), similar to

Strategy 1. However, individuals misclassified

as low-risk individuals do not increase dramati-

cally overall costs, since it becomes a natural an

overhead already accounted for in the method.

Misclassifying high-risk individuals leads to

incorrectly applying Strategy 1 to a healthy

individual, or to applying a pooling technique

to a group with at least one infected individual.

We observe how the pre-classifier helps to

reduce the total cost of identifying correctly the

high-risk individuals. When the pre-classifier

has high levels of sensitivity and specificity,

we achieve outcomes similar to the Strategy 1

with a small overhead due to the cost intro-

duced by pooling. Again, as the model becomes

more accurate, this overhead decreases. Sen-

sitivity and specificity play the same role as

in Strategy 1.

In Strategy 3 (SMG3), total costs are higher

than the Strategy 1 or Strategy 2 due to massive

testing with antigen-based technologies for the

low-risk group. Even if it is possible to clas-

sify correctly most of the population according

to their risk, the minimum will be of at least

$ 60 000 for each 1 000 individuals.

Finally, Strategy 4 has a similar cost struc-

ture compared with the pooling scheme in

Strategy 2 (Fig. 2). Using maximally targeted

technologies to each type of patient is similar

to applying complex (and difficult) techniques

like pooling. Given that we use antigen-based

testing without the restriction of incubation

periods, sensitivity is the only factor affecting

the sign of the slope.

Positive Cases Reported: In the case of

positive reported, Strategy 1 performs well

with a prior classification. Even when ignoring

low-risk individuals, we capture almost all true

positives when the sensitivity and specific-

ity of the model is 90 %. Sensitivity helps to

discard potential true negatives because it has

determined correctly the most possible positive

cases. When the sensitivity is low, the strat-

egy misses those true positives, who are thus

left untested.

Strategy 2 includes the low-risk individuals

(SMG4), with an increase in positive reported

from the start, decreasing the number of mis-

matches. Even when the classifier has low

sensitivity and specificity, pooling captures the

infected individuals identified as low-risk at

the expense of higher costs than only using

RT-qPCR.

Strategy 3 (SMG5) increases detection of

true positives even more regarding Strategy 1,

specially at low prevalence contingent on reach-

ing high sensitivity (90 %); the number of false

negatives increases at high prevalence below

this sensitivity value. A large group of infected

individuals are declared as low-risk. Combined

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

with the application of antigen-based tests

which have lower sensitivity than RT-qPCR

ones, the probability of capturing true positives

is reduced.

Strategy 4 (Fig. 3) shows a similar pattern

as Strategies 1 and 3. We observe that all func-

tions are concave, implying improvements in

detection as prevalence increases for sensitivity

beyond 60 %. Even when the outcome of the

classifier resembles that of Strategies 1 and 3,

the robustness of the curves, indicates that RT-

LAMP reduces the variability introduced by

antigen-based testing.

Finally, we observe that sensitivity below

50 % appears to yield convex curves for the

number of positives reported, while curves

Fig. 2. Total cost according to Strategy 4. Introducing RT-LAMP testing significantly decreases total costs compared with all

other strategies.

Fig. 3. Number of reported as positive according to Strategy 4. Introducing RT-LAMP testing yields concave curves at all

sensitivity and specificity values. Outcomes are qualitatively similar to Strategies 1 and 3.

14 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

corresponding to values above 50 % seem to be

all concave for strategies 1-3; this is modulated

by the number of antigen-based tests when

applicable. This is significant, since it delineates

a response function in terms of testing efforts

needed at a certain value of prevalence given a

current combination of resources. The higher

the prevalence, the more likely it is to increase

detection of true positives. Similarly, the more

antigen-based tests are used, the more likely

false negatives will appear. However, it also

implies that the impact of RT-LAMP and simi-

lar technologies is significant, since even at low

sensitivity of the classifier the effort function

is concave.

Number of Tests per Person: For Strat-

egy 1 (SMG6), the number of tests per person

obtained with computational experiments is

as expected. The less accurate the model in

identifying high-risk individuals, the larger

the number of tests needs to be spent, given

the confirmatory mechanism of antigen-based

testing against RT-qPCR. When the model is

poorly fitted, the strategy spends around 1.2-1.7

tests per person. As the model sensitivity and

specificity increases, the curves approach 1 at

high prevalence. In all scenarios, the number of

tests per person is high (1.2-1.7) at low preva-

lence, since negatives are the majority, and the

strategy must spend two tests to confirm true

positives.

When pooling is introduced (SMG7), a 0.5

reduction in average occurs when the model

is correctly fitted regarding Strategy 1. Speci-

ficity controls the behavior of the curve in

terms of convexity and slope. Low specificity

increases misclassification of low-risk individu-

als, increasing the detection of true positives in

the pooling technique.

The number of tests per person in Strat-

egy 3 descends linearly as specificity increases

(SMG8). Compared against Strategy 1, mul-

tiple testing can be reduced if the model is

well-fitted. Strategy 2, in contrast, maintains

better performance in this aspect. A similar

pattern occurs in Strategy 4 (SMG9). However,

it is worth noting that the number of tests per

person remains relatively constant and close to

1 when the classifier shows high sensitivity and

specificity in both Strategies. This is significant,

since the resulting curve indicates scalability.

Performance across strategies: To com-

pare the relative performance across different

strategies, we establish two new quantities,

which we call stock capacity () and detection

efficiency (). To do so, we define an amortiza-

tion index per Strategy i ∈ {1,2,3,4}

where TiTotal is the total number of tests per-

formed by that Strategy. The left-most factor

in Si represents the buying power of testing

per each dollar spent. The right-most factor

scales the number to the effectively covered

population. This is the case of Strategy 1 where

it only considers the high-risk population. For

instance, if Si = 0.01 and the budget is $

100 000, then the healthcare system can only

afford Si × 100 000 = 100 tests in total accord-

ing to each strategy (a mix between RT-qPCR,

Antigen and RT-LAMP). Meanwhile, for i ∈

{1,2,3,4} the detection efficiency is

We interpret the index as the capacity

of each strategy to detect a positive case per

each dollar spend. Similar to, Si the number

is scaled to the effective population covered.

In the case of a value Si = 0.001, and plans to

spend $ 100 000 in the strategy, we can expect

to detect Ei × $ 100 000 = 10 positive cases.

(Fig. 4) shows the values of Si and Ei across all

the strategies. We set here a fixed budget of

$ 100 000. The red arrow (or point) represents

a base case, with detection of 1 000 × P(DP)

positive cases spending $ (100 × 1 000 × P(DP))

using only RT-qPCR tests. Arrows per strategy

(i.e., hues of blue) indicate prevalence increase.

Comparison of the four strategies regarding

the stock capacity versus their detection effi-

ciency. Arrows go from 0 (start) to 30 % (point)

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

prevalence. Red arrows (or single triangle) rep-

resent the perfect case when there are positives,

with a cost of $ (1 000 × P(DP) × 100) using

only RT-qPCR.

Strategy 1 shows its weakness due to the

small capacity to buy tests and overall effec-

tiveness. In other words, there is no difference

between using antigen-based testing or one

using only RT-qPCR if only patients classified

as high-risk are tested in the best-case scenario

and significantly deteriorate when the classifier

performs poorly. Meanwhile, Strategies 2 and 3

increase their capacities by covering the weak

points from Strategy 1. Pooling (Strategy 2)

increases the capacity of detection by maintain-

ing the number of tests stable. Retesting (Strat-

egy 3) is inefficient to capture positive cases

even when the number of tests is still small.

This is explained due to the low sensitivity of

antigen-based tests, around 80 %.

Finally, Strategy 4 presents an augmented

buying power of tests and detection efficiency.

Targeting technologies to specific pre-selected

groups appears to be the best strategy to maxi-

mize budget impacts across healthcare systems.

DISCUSSION

In this study, we investigated the theoretical

impact of four different mass testing strategies

for COVID-19 in Costa Rica, incorporating a

pre-classification mechanism to improve test-

ing efficiency. By simulating these strategies,

we evaluated their overall costs, the number of

positive cases detected, and the number of tests

required per person. Our pre-classifier, based

on machine learning methods, stratifies the

population into high-risk and low-risk groups

using variables such as social determinants,

while maintaining patient privacy and infor-

mation security. Furthermore, we reformu-

lated the outcomes of each strategy in terms

of purchasing power (i.e., stock capacity) and

detection efficiency per dollar spent, providing

a comprehensive analysis of resource allocation

during a pandemic.

Fig. 4. Comparison of the four strategies with respect to the stock capacity versus their detection efficiency. Arrows go from 0

(start) to 30 % (point). Red arrows (or single triangle) represent the perfect case when there is 1 000 people × P (DP) positives,

with a cost of 1 000 people × P (DP) × $ 100 using only RT-qPCR.

16 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

Our research gains significance when

viewed in the context of previous public health

interventions that have leveraged predictive

modeling and mass testing. For instance, Jehi

et al. (2020) developed a risk prediction model

to prioritize COVID-19 testing based on indi-

vidual patient characteristics, improving testing

efficiency and resource allocation. Similarly,

Schwab et al. (2020) reviewed clinical predic-

tive models for COVID-19, highlighting the

potential of machine learning approaches in

enhancing patient triage and clinical decision-

making. Our work extends these insights by

quantitatively comparing different testing strat-

egies and showing how a well-fitted classifier

can significantly reduce costs and increase the

detection of positive cases. Finally, Huang et al.

(2022) proved that a data-driven testing strat-

egy can detect 89.35 % of positives with only

48.1 7% of the available resources.

The introduction of a predictive model or

classifier brings two strategic advantages. First,

it can reduce overall costs, time and human

efforts. Second, it increases information rich-

ness across the testing process. The first advan-

tage relates to the system capacity to choose the

best and cheaper technology according to each

patient. If the model classifies individuals cor-

rectly, testing efforts can be optimized. Further-

more, healthcare systems can cover deficiencies

present in one technology with the advantages

of another (i.e., scalability), using the probabi-

listic prediction of the classifier as a triaging

device while waiting for laboratory tests to

finish and confirm or reject the result. Having

more data, and consequently better predic-

tion capabilities, allows clustering individuals

into subgroups according to particular features

such as their social, demographic or economic

indicators and mobility patterns, among others.

This information could lead healthcare authori-

ties to adopt more personalized measures to

cover certain vulnerable groups.

Our results show that all the strategies

become more effective when the classifier

-arguably a sophisticated machine learning

method-is well-fitted, reaching sensitivity and

specificity levels of 60 % or higher. We showed

that sensitivity (identification of potential posi-

tives) plays a crucial role in reducing costs and

increasing confirmation of positives. For the

pooling scenario, specificity controls the num-

ber of tests per person.

One of the fundamental limitations of

achieving a good fit for such models is access

to high-quality individual data. The quality of

data remains a challenge since the beginning

of the pandemic, particularly in developing

nations and emerging economies. For instance

(Wynants et al., 2020) reviewed more than

126 000 studies related to COVID-19 prog-

nosis prediction which only USA, Brazil and

Mexico have formal studies about it. Available

data tend to only reflect the reality of people

who have undergone testing, and even when

that is the case, datasets are biased by the

administrative reality -and shortcomings- of

the specific healthcare system. Therefore, we

can expect a similar systematic bias in the clas-

sification process due to the different epidemio-

logical moments across the pandemic. Testing

increases during high-peak waves, confirming

symptomatic patients and capturing asymp-

tomatic nexus of them. When the pandemic

wave passes and minimum cases are reached,

the testing strategy tends to focus on confirm-

ing symptomatic cases arriving at clinical cen-

ters. During these periods, the real number of

infected asymptomatic people remains unclear.

In addition, overloading of the healthcare ser-

vices impacts data production, which may be

ready for consumption days or weeks later. This

requires, as proposed, adjusting the model to

correct for administrative and systematic lags.

Another set of limitations corresponds

to the choice of potential classification mod-

els as well. We mention a non-exhaustive list

of classification methods with their respec-

tive advantages and disadvantages. The classic

logistic regression model is easy to implement,

but the implicit assumptions and the inclusion

of administrative lags in the data can negatively

impact the interpretability of results due to an

artificial increase in the number of coefficients;

in this situation, a Ridge, or Lasso regular-

ization could reduce their number. Another

Revista de Biología Tropical, ISSN: 2215-2075, Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

option, if the data exhibits non-linearity, is to

use a support vector machine (SVM), which

can handle situations in which classes are not-

linearly separable. The downside here is the

computational cost during the training stage,

which has to be performed a limited num-

ber of times as the pandemic evolves. Tree

ensemble approaches are popular, including

Random Forest, XGboost, and Gradient Boost-

ing. In practice, these methods perform better

than the mentioned classifiers but require fine-

tuning of hyperparameters whose interpreta-

tion may not be direct. Finally, deep-learning

algorithms can be used to fit the classifier at

the expense of complexity and interpretability

(Escobar et al., 2022).

We envision a series of challenges in the

implementation of a classification system such

as that described here. The main one is the

adoption of machine learning assisted system

by clinical and health policy authorities to tri-

age the population before performing labora-

tory tests. While unforeseen clinical or ethical

reasons may hamper the implementation of the

model, the aim of this statistical approach is to

become a companion instead of a competitor

for healthcare providers. The advantage of clas-

sification-assisted triaging of patients in clinical

contexts has been discussed and demonstrated

in literature (Jehi et al., 2020; Schwab et al.,

2020). Having some prior information about

the possible test result can better prepare clini-

cians and staff to handle wave peaks efficient-

ly, allocate resources more appropriately, and

anticipate critical resource usage and patient

mortality counterfactuals. Another challenge is

the actual capacity of systems to triage patients.

Even with an algorithm ready, further studies

are needed about how to integrate it into work-

flows across medical centers and public health

authorities. In the particular case of Costa

Rica, the EDUS (Expediente Digital Único

en Salud) system can serve as the channel to

deliver results from the algorithm to laboratory

technicians and physicians. However, creation

of a new submodule will require testing, vali-

dation and data assurance in compliance with

information security standards in the public

health service (CCSS). Even if the EDUS system

already collects most of the information about

patients, the process of anonymizing, handling,

securing, and ensuring responsible use of per-

sonal information must remain as a top prior-

ity. Finally, the attitude of the public around

collection of information and its handling con-

stitutes a challenge of uncertain proportions.

Our next step is to fit a classifier using

both real and synthetic datasets. The EDUS is

the main source of individual data of the Costa

Rican public health. When a patient arrives at

a medical appointment, physicians register the

health status, diagnosis, demographic and relat-

ed factors of each patient. During the COVID-

19 pandemic, the tool was used to track down

the symptoms across the population, to provide

hot-lines for medical support and to validate

the number of vaccines already applied. We

believe this information source can be responsi-

bly used further in benefit of all users. Its main

advantage is the massive information den-

sity and patient coverage. Given the universal

healthcare system in Costa Rica, information

about a wide range of groups exists regard-

less of economic status. Another secondary

corresponds to the Instituto Costarricense de

Investigación y Enseñanza en Nutrición y Salud

(INCIENSA: National Institute of Research and

Education on Nutrition and Health). At the

beginning of the pandemic, INCIENSA col-

lected numerous COVID-19 samples alongside

epidemiological and sociodemographic data of

infected patients. Even if the diversity in this

source is less than that of EDUS, it could be an

important source to adjust the model.

Finally, we expect to develop synthetic

datasets through simulation. Prior experience

with agent-based modeling (Núñez-Corrales

& Jakobsson, 2020) showed that it is pos-

sible to replicate features of epidemic waves

and the effect of public policy measures in

silico, to then overlay our strategies and deter-

mine performance under various scenarios

and constraints; other methods exist and will

be explored. These datasets can be openly

shared across all relevant stakeholders with-

out risking healthcare data leaks, while still

18 Revista de Biología Tropical, ISSN: 2215-2075 Vol. 73: e57971, enero-diciembre 2025 (Publicado Mar. 19, 2025)

being representative of aggregate statistics of

the underlying population.

These strategies may extend beyond

COVID-19 to other infectious diseases with

similar transmission characteristics, such as

influenza and tuberculosis. Targeted test-

ing combined with predictive modeling can

improve the efficient use of testing resourc-

es and enable timely interventions. However,

effectiveness depends on disease-specific fac-

tors like incubation periods, transmission rates,

and modes of transmission. For diseases with

longer incubation periods or different trans-

mission modes (e.g., vector-borne diseases like

malaria), adjustments to predictive models and

testing protocols may be necessary. Future

research should focus on adapting these meth-

odologies to a broader range of pathogens

to enhance applicability across various public

health contexts.

Ethical statement: the authors declare that

they all agree with this publication and made

significant contributions; that there is no con-

flict of interest of any kind; and that we fol-

lowed all pertinent ethical and legal procedures

and requirements. All financial sources are fully

and clearly stated in the acknowledgments sec-

tion. A signed document has been filed in the

journal archives.

See supplementary material

a17v73n1-suppl1

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