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.

Forecasting hospital discharges for respiratory conditions in Costa Rica using climate and pollution data.

Respiratory diseases represent one of the most significant economic burdens on healthcare systems worldwide. The variation in the increasing number of cases depends greatly on climatic seasonal effects, socioeconomic factors, and pollution. Therefore, understanding these variations and obtaining precise forecasts allows health authorities to make correct decisions regarding the allocation of limited economic and human resources. We aimed to model and forecast weekly hospitalizations due to respiratory conditions in seven regional hospitals in Costa Rica using four statistical learning techniques (Random Forest, XGboost, Facebook's Prophet forecasting model, and an ensemble method combining the above methods), along with 22 climate change indices and aerosol optical depth as an indicator of pollution. Models were trained using data from 2000 to 2018 and were evaluated using data from 2019 as testing data. During the training period, we set up 2-year sliding windows and a 1-year assessment period, along with the grid search method to optimize hyperparameters for each model. The best model for each region was selected using testing data, based on predictive precision and to prevent overfitting. Prediction intervals were then computed using conformal inference. The relative importance of all climatic variables was computed for the best model, and similar patterns in some of the seven regions were observed based on the selected model. Finally, reliable predictions were obtained for each of the seven regional hospitals.

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.

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.

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.

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.