Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.

Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country's Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission's spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

A mathematical model with nonlinear relapse: conditions for a forward-backward bifurcation.

We constructed a Susceptible-Addicted-Reformed model and explored the dynamics of nonlinear relapse in the Reformed population. The transition from susceptible considered at-risk is modeled using a strictly decreasing general function, mimicking an influential factor that reduces the flow into the addicted class. The basic reproductive number is computed, which determines the local asymptotically stability of the addicted-free equilibrium. Conditions for a forward-backward bifurcation were established using the basic reproductive number and other threshold quantities. A stochastic version of the model is presented, and some numerical examples are shown. Results showed that the influence of the temporarily reformed individuals is highly sensitive to the initial addicted population.

An implementation of a multilayer network model for the Covid-19 pandemic: A Costa Rica study.

We present a numerical implementation for a multilayer network to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social and sporadic networks) and includes an SEIR type model for the transmission of the virus. The algorithm described in this paper includes the main ideas of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica by simulating extensive possible scenarios and projections. We include two simulations: a study of the effect of restrictions on the transmission of the virus and a Costa Rica case study that was shared with the Costa Rican health authorities.

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica.

Objective: To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods: Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results: The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions: Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica

[ABSTRACT]. Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

[RESUMEN]. Objetivo. Resumir los resultados de las investigaciones realizadas en Costa Rica en las que se aplicaron métodos matemáticos y estadísticos para estudiar la dinámica de transmisión de las enfermedades transmitidas por mosquitos. Métodos. Se seleccionaron y analizaron tres artículos con análisis matemáticos y estadísticos sobre enfermedades transmitidas por vectores en Costa Rica. En estos artículos se muestra el valor y la pertinencia de emplear diferentes métodos cuantitativos para comprender la dinámica de la enfermedad y brindar apoyo a la toma de decisiones. Resultados. Los resultados de estas investigaciones: 1) muestran la repercusión en los informes de casos de dengue cuando surge un segundo agente patógeno, como el chikunguña; 2) recuperan parámetros clave en la dinámica del Zika mediante la inferencia bayesiana; y 3) muestran el uso de los algoritmos de aprendizaje automático y las variables climáticas para pronosticar el riesgo relativo de dengue en cinco lugares diferentes. Conclusiones. Los modelos matemáticos y estadísticos permiten describir la dinámica de transmisión de las enfermedades transmitidas por mosquitos, mediante la provisión de información cuantitativa para brindar apoyo a los métodos de prevención y control y a la planificación de la asignación de recursos.

[RESUMO]. Objetivo. Resumir os resultados de estudos realizados na Costa Rica em que foram aplicados métodos matemáticos e estatísticos para estudar a dinâmica de transmissão de doenças transmitidas por mosquitos. Métodos. Foram selecionados e revisados três artigos com análises matemáticas e estatísticas sobre doenças transmitidas por vetores na Costa Rica. Esses artigos mostram o valor e a pertinência do uso de diferentes métodos quantitativos para compreender a dinâmica das doenças e apoiar a tomada de decisões. Resultados. Os resultados dessas investigações: 1) mostram o impacto nas notificações de casos de dengue quando surge um segundo patógeno, como o chikungunya; 2) recuperam parâmetros-chave na dinâmica do zika, usando a inferência bayesiana; e 3) mostram o uso de algoritmos de aprendizagem por máquina e variáveis climáticas para prever o risco relativo da dengue em cinco locais diferentes. Conclusões. A modelagem matemática e estatística permite a descrição da dinâmica de transmissão de doenças transmitidas por mosquitos ao oferecer informações quantitativas para apoiar métodos de prevenção e/ou controle e o planejamento da alocação de recursos.

A multilayer network model of Covid-19: Implications in public health policy in Costa Rica.

Successful partnerships between researchers, experts, and public health authorities have been critical to navigate the challenges of the Covid-19 pandemic worldwide. In this collaboration, mathematical models have played a decisive role in informing public policy, with findings effectively translated into public health measures that have shaped the pandemic in Costa Rica. As a result of interdisciplinary and cross-institutional collaboration, we constructed a multilayer network model that incorporates a diverse contact structure for each individual. In July 2020, we used this model to test the effect of lifting restrictions on population mobility after a so-called "epidemiological fence" imposed to contain the country's first big wave of cases. Later, in August 2020, we used it to predict the effects of an open and close strategy (the Hammer and Dance). Scenarios constructed in July 2020 showed that lifting restrictions on population mobility after less than three weeks of epidemiological fence would produce a sharp increase in cases. Results from scenarios in August 2020 indicated that the Hammer and Dance strategy would only work with 50% of the population adhering to mobility restrictions. The development, evolution, and applications of a multilayer network model of Covid-19 in Costa Rica has guided decision-makers to anticipate implementing sanitary measures and contributed to gain valuable time to increase hospital capacity.

Projecting the impact of Covid-19 variants and vaccination strategies in disease transmission using a multilayer network model in Costa Rica.

For countries starting to receive steady supplies of vaccines against SARS-CoV-2, the course of Covid-19 for the following months will be determined by the emergence of new variants and successful roll-out of vaccination campaigns. To anticipate this scenario, we used a multilayer network model developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and to estimate the impact of the introduction of the Delta variant in the country, under two plausible vaccination scenarios, one sustaining Costa Rica's July 2021 vaccination pace of 30,000 doses per day and with high acceptance from the population and another with declining vaccination pace to 13,000 doses per day and with lower acceptance. Results suggest that the introduction and gradual dominance of the Delta variant would increase Covid-19 hospitalizations and ICU admissions by [Formula: see text] and [Formula: see text], respectively, from August 2021 to December 2021, depending on vaccine administration and acceptance. In the presence of the Delta variant, new Covid-19 hospitalizations and ICU admissions are estimated to increase around [Formula: see text] and [Formula: see text], respectively, in the same period if the vaccination pace drops. Our results can help decision-makers better prepare for the Covid-19 pandemic in the months to come.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica / Modelos matemáticos y estadísticos para el control de enfermedades transmitidas por mosquitos: la experiencia de Costa Rica / Modelos matemáticos e estatísticos para o controle de doenças transmitidas por mosquitos: a experiência da Costa Rica

ABSTRACT Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

RESUMEN Objetivo. Resumir los resultados de las investigaciones realizadas en Costa Rica en las que se aplicaron métodos matemáticos y estadísticos para estudiar la dinámica de transmisión de las enfermedades transmitidas por mosquitos. Métodos. Se seleccionaron y analizaron tres artículos con análisis matemáticos y estadísticos sobre enfermedades transmitidas por vectores en Costa Rica. En estos artículos se muestra el valor y la pertinencia de emplear diferentes métodos cuantitativos para comprender la dinámica de la enfermedad y brindar apoyo a la toma de decisiones. Resultados. Los resultados de estas investigaciones: 1) muestran la repercusión en los informes de casos de dengue cuando surge un segundo agente patógeno, como el chikunguña; 2) recuperan parámetros clave en la dinámica del Zika mediante la inferencia bayesiana; y 3) muestran el uso de los algoritmos de aprendizaje automático y las variables climáticas para pronosticar el riesgo relativo de dengue en cinco lugares diferentes. Conclusiones. Los modelos matemáticos y estadísticos permiten describir la dinámica de transmisión de las enfermedades transmitidas por mosquitos, mediante la provisión de información cuantitativa para brindar apoyo a los métodos de prevención y control y a la planificación de la asignación de recursos.

RESUMO Objetivo. Resumir os resultados de estudos realizados na Costa Rica em que foram aplicados métodos matemáticos e estatísticos para estudar a dinâmica de transmissão de doenças transmitidas por mosquitos. Métodos. Foram selecionados e revisados três artigos com análises matemáticas e estatísticas sobre doenças transmitidas por vetores na Costa Rica. Esses artigos mostram o valor e a pertinência do uso de diferentes métodos quantitativos para compreender a dinâmica das doenças e apoiar a tomada de decisões. Resultados. Os resultados dessas investigações: 1) mostram o impacto nas notificações de casos de dengue quando surge um segundo patógeno, como o chikungunya; 2) recuperam parâmetros-chave na dinâmica do zika, usando a inferência bayesiana; e 3) mostram o uso de algoritmos de aprendizagem por máquina e variáveis climáticas para prever o risco relativo da dengue em cinco locais diferentes. Conclusões. A modelagem matemática e estatística permite a descrição da dinâmica de transmissão de doenças transmitidas por mosquitos ao oferecer informações quantitativas para apoiar métodos de prevenção e/ou controle e o planejamento da alocação de recursos.

The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica.

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.