Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches

Investigating the Determinants of Dengue Outbreak in Oman: A Study in Seeb

OBJECTIVE

This study investigates the key factors contributing to the dengue outbreak in Oman.

METHODS

Data on climate (e.g., temperature, humidity, wind pace), population traits (e.g., populace density), and vector dynamics (e.g., mosquito density) within the Seeb district of Oman from 2022 to 2023 were gathered. The partial least squares structural equation modeling (PLS-SEM) was performed to study which variables affect dengue outbreaks.

RESULTS

The results indicate that climatic factors significantly affect the dengue vector (β = -0.361, p < 0.001) but do not directly impact the dengue outbreak. Population characteristics, however, have a more substantial impact on dengue transmission, with a total effect (β = 0.231, p = 0.002) being relatively higher than that of the vector itself (total effect: β = 0.116, p < 0.001).

CONCLUSIONS

Even with ongoing vector intervention efforts, the study underscores the need to include innovative public health interventions when considering environmental and demographic factors. More advantageous surveillance and focused interventions in excessive-threat regions are essential to mitigate the effect of dengue in Oman.

A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping

System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.

The timeline of overseas imported cases acts as a strong indicator of dengue outbreak in mainland China

The emergence of dengue viruses in new, susceptible human populations worldwide is increasingly influenced by a combination of local and global human movements and favorable environmental conditions. While various mathematical models have explored the impact of environmental factors on dengue outbreaks, the significant role of human mobility both internationally and domestically in transmitting the disease has been less frequently addressed. In this context, we introduce a modeling framework that integrates the effects of international travel-induced imported cases, climatic conditions, and local human movements to assess the spatiotemporal dynamics of dengue transmission. Utilizing the generation matrix method, we calculate the basic reproduction number and its sensitivity to various model parameters. Through numerical simulations using data on climate, human mobility, and reported dengue cases in mainland China, our model demonstrates a good agreement with observed data upon validation. Our findings reveal that while climatic conditions are a key driver for the rapid dengue transmission, human mobility plays a crucial role in its local spread. Importantly, the model highlights the significant impact of imported cases from overseas on the initiation of dengue outbreaks and their contribution to increasing the disease incidence rate by 34.6%. Furthermore, the analysis identifies that dengue cases originating from regions, such as Cambodia and Myanmar internationally, and Guangzhou and Xishuangbanna domestically, have the potential to significantly increase the disease burden in mainland China. These insights emphasize the critical need to include data on imported cases and domestic travel patterns in disease outbreak models to improve the precision of predictions, thereby enhancing dengue prevention, surveillance, and response strategies.

Economic optimization of Wolbachia-infected Aedes aegypti release to prevent dengue

BACKGROUND

Dengue virus, primarily transmitted by the Aedes aegypti mosquito, is a major public health concern affecting ≈3.83 billion people worldwide. Recent releases of Wolbachia-transinfected Ae. aegypti in several cities worldwide have shown that it can reduce dengue transmission. However, these releases are costly, and, to date, no framework has been proposed for determining economically optimal release strategies that account for both costs associated with disease risk and releases.

RESULTS

We present a flexible stochastic dynamic programming framework for determining optimal release schedules for Wolbachia-transinfected mosquitoes that balances the cost of dengue infection with the costs of rearing and releasing transinfected mosquitoes. Using an ordinary differential equation model of Wolbachia and dengue in a hypothetical city loosely describing areas at risk of new dengue epidemics, we determined that an all-or-nothing release strategy that quickly brings Wolbachia to fixation is often the optimal solution. Based on this, we examined the optimal facility size, finding that it was inelastic with respect to the mosquito population size, with a 100% increase in population size resulting in a 50-67% increase in optimal facility size. Furthermore, we found that these results are robust to mosquito life-history parameters and are mostly determined by the mosquito population size and the fitness costs associated with Wolbachia.

CONCLUSIONS

These results reinforce that Wolbachia-transinfected mosquitoes can reduce the cost of dengue epidemics. Furthermore, they emphasize the importance of determining the size of the target population and fitness costs associated with Wolbachia before releases occur. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A Coupled Statistical and Deterministic Model for Forecasting Climate-Driven Dengue Incidence in Selangor, Malaysia

The mosquito-borne dengue virus remains a major public health concern in Malaysia. Despite various control efforts and measures introduced by the Malaysian Government to combat dengue, the increasing trend of dengue cases persists and shows no sign of decreasing. Currently, early detection and vector control are the main methods employed to curb dengue outbreaks. In this study, a coupled model consisting of the statistical ARIMAX model and the deterministic SI-SIR model was developed and validated using the weekly reported dengue data from year 2014 to 2019 for Selangor, Malaysia. Previous studies have shown that climate variables, especially temperature, humidity, and precipitation, were able to influence dengue incidence and transmission dynamics through their effect on the vector. In this coupled model, climate is linked to dengue disease through mosquito biting rate, allowing real-time forecast of dengue cases using climate variables, namely temperature, rainfall and humidity. For the period chosen for model validation, the coupled model can forecast 1-2 weeks in advance with an average error of less than 6%, three weeks in advance with an average error of 7.06% and four weeks in advance with an average error of 8.01%. Further model simulation analysis suggests that the coupled model generally provides better forecast than the stand-alone ARIMAX model, especially at the onset of the outbreak. Moreover, the coupled model is more robust in the sense that it can be further adapted for investigating the effectiveness of various dengue mitigation measures subject to the changing climate.

Transmission dynamics of a reaction-advection-diffusion dengue fever model with seasonal developmental durations and intrinsic incubation periods

In this paper, we propose a reaction-advection-diffusion dengue fever model with seasonal developmental durations and intrinsic incubation periods. Firstly, we establish the well-posedness of the model. Secondly, we define the basic reproduction number ℜ 0 for this model and show that ℜ 0 is a threshold parameter: ifℜ 0 < 1 , then the disease-free periodic solution is globally attractive; ifℜ 0 > 1 , the system is uniformly persistent. Thirdly, we study the global attractivity of the positive steady state when the spatial environment is homogeneous and the advection of mosquitoes is ignored. As an example, we use the model to investigate the dengue fever transmission case in Guangdong Province, China, and explore the impact of model parameters on ℜ 0 . Our findings indicate that ignoring seasonality may underestimate ℜ 0 . Additionally, the spatial heterogeneity of transmission may increase the risk of disease transmission, while the increase of seasonal developmental durations, intrinsic incubation periods and advection rates can all reduce the risk of disease transmission.

Shifting patterns of dengue three years after Zika virus emergence in Brazil

In 2015, the Zika virus (ZIKV) emerged in Brazil, leading to widespread outbreaks in Latin America. Following this, many countries in these regions reported a significant drop in the circulation of dengue virus (DENV), which resurged in 2018-2019. We examine age-specific incidence data to investigate changes in DENV epidemiology before and after the emergence of ZIKV. We observe that incidence of DENV was concentrated in younger individuals during resurgence compared to 2013-2015. This trend was more pronounced in Brazilian states that had experienced larger ZIKV outbreaks. Using a mathematical model, we show that ZIKV-induced cross-protection alone, often invoked to explain DENV decline across Latin America, cannot explain the observed age-shift without also assuming some form of disease enhancement. Our results suggest that a sudden accumulation of population-level immunity to ZIKV could suppress DENV and reduce the mean age of DENV incidence via both protective and disease-enhancing interactions.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Effects of boosting and waning in highly exposed populations on dengue epidemic dynamics

Sequential infection with multiple dengue virus (DENV) serotypes is thought to induce enduring protection against dengue disease. However, long-term antibody waning has been observed after repeated DENV infection. Here, we provide evidence that highly immune Nicaraguan children and adults (n = 4478) experience boosting and waning of antibodies during and after major Zika and dengue epidemics. We develop a susceptible-infected-recovered-susceptible (SIRS-type) model that tracks immunity by titer rather than number of infections to show that boosts in highly immune individuals can contribute to herd immunity, delaying their susceptibility to transmissible infection. In contrast, our model of lifelong immunity in highly immune individuals, as previously assumed, results in complete disease eradication after introduction. Periodic epidemics under this scenario can only be sustained with a constant influx of infected individuals into the population or a high basic reproductive number. We also find that Zika virus infection can boost DENV immunity and produce delays and then surges in dengue epidemics, as observed with real epidemiological data. This work provides insight into factors shaping periodicity in dengue incidence and may inform vaccine efforts to maintain population immunity.

Temporary Cross-Immunity as a Plausible Driver of Asynchronous Cycles of Dengue Serotypes

Many infectious diseases exist as multiple variants, with interactions between variants potentially driving epidemiological dynamics. These diseases include dengue, which infects hundreds of millions of people every year and exhibits complex multi-serotype dynamics. Antibodies produced in response to primary infection by one of the four dengue serotypes can produce a period of temporary cross-immunity (TCI) to infection by other serotypes. After this period, the remaining antibodies can facilitate the entry of heterologous serotypes into target cells, thus enhancing severity of secondary infection by a heterologous serotype. This represents antibody-dependent enhancement (ADE). In this study, we analyze an epidemiological model to provide novel insights into the importance of TCI and ADE in producing cyclic outbreaks of dengue serotypes. Our analyses reveal that without TCI, such cyclic outbreaks are synchronous across serotypes and only occur when ADE produces high transmission rates. In contrast, the presence of TCI allows asynchronous cycles of serotypes by inducing a time lag between recovery from primary infection by one serotype and secondary infection by another, with such cycles able to occur without ADE. Our results suggest that TCI is a fundamental driver of asynchronous cycles of dengue serotypes and possibly other multi-variant diseases.

Modelling the transmission of dengue, zika and chikungunya: a scoping review protocol

INTRODUCTION

Aedes mosquitoes are the primary vectors for the spread of viruses like dengue (DENV), zika (ZIKV) and chikungunya (CHIKV), all of which affect humans. Those diseases contribute to global public health issues because of their great dispersion in rural and urban areas. Mathematical and statistical models have become helpful in understanding these diseases' epidemiological dynamics. However, modelling the complexity of a real phenomenon, such as a viral disease, should consider several factors. This scoping review aims to document, identify and classify the most important factors as well as the modelling strategies for the spread of DENV, ZIKV and CHIKV.

METHODS AND ANALYSIS

We will conduct searches in electronic bibliographic databases such as PubMed, MathSciNet and the Web of Science for full-text peer-reviewed articles written in English, French and Spanish. These articles should use mathematical and statistical modelling frameworks to study dengue, zika and chikungunya, and their cocirculation/coinfection with other diseases, with a publication date between 1 January 2011 and 31 July 2023. Eligible studies should employ deterministic, stochastic or statistical modelling approaches, consider control measures and incorporate parameters' estimation or considering calibration/validation approaches. We will exclude articles focusing on clinical/laboratory experiments or theoretical articles that do not include any case study. Two reviewers specialised in zoonotic diseases and mathematical/statistical modelling will independently screen and retain relevant studies. Data extraction will be performed using a structured form, and the findings of the study will be summarised through classification and descriptive analysis. Three scoping reviews will be published, each focusing on one disease and its cocirculation/co-infection with other diseases.

ETHICS AND DISSEMINATION

This protocol is exempt from ethics approval because it is carried out on published manuscripts and without the participation of humans and/or animals. The results will be disseminated through peer-reviewed publications and presentations in conferences.

Dynamics and optimal control of a stochastic Zika virus model with spatial diffusion

Zika is an infectious disease with multiple transmission routes, which is related to severe congenital disabilities, especially microcephaly, and has attracted worldwide concern. This paper aims to study the dynamic behavior and optimal control of the disease. First, we establish a stochastic reaction-diffusion model (SRDM) for Zika virus, including human-mosquito transmission, human-human sexual transmission, and vertical transmission of mosquitoes, and prove the existence, uniqueness, and boundedness of the global positive solution of the model. Then, we discuss the sufficient conditions for disease extinction and the existence of a stationary distribution of positive solutions. After that, three controls, i.e. personal protection, treatment of infected persons, and insecticides for spraying mosquitoes, are incorporated into the model and an optimal control problem of Zika is formulated to minimize the number of infected people, mosquitoes, and control cost. Finally, some numerical simulations are provided to explain and supplement the theoretical results obtained.

The biting rate of Aedes aegypti and its variability: A systematic review (1970-2022)

BACKGROUND

Transmission models have a long history in the study of mosquito-borne disease dynamics. The mosquito biting rate (MBR) is an important parameter in these models, however, estimating its value empirically is complex. Modeling studies obtain biting rate values from various types of studies, each of them having its strengths and limitations. Thus, understanding these study designs and the factors that contribute to MBR estimates and their variability is an important step towards standardizing these estimates. We do this for an important arbovirus vector Aedes aegypti.

METHODOLOGY/PRINCIPAL FINDINGS

We perform a systematic review using search terms such as 'biting rate' and 'biting frequency' combined with 'Aedes aegypti' ('Ae. aegypti' or 'A. aegypti'). We screened 3,201 articles from PubMed and ProQuest databases, of which 21 met our inclusion criteria. Two broader types of studies are identified: human landing catch (HLC) studies and multiple feeding studies. We analyze the biting rate data provided as well as the methodologies used in these studies to characterize the variability of these estimates across temporal, spatial, and environmental factors and to identify the strengths and limitations of existing methodologies. Based on these analyses, we present two approaches to estimate population mean per mosquito biting rate: one that combines studies estimating the number of bites taken per gonotrophic cycle and the gonotrophic cycle duration, and a second that uses data from histological studies. Based on one histological study dataset, we estimate biting rates of Ae. aegypti (0.41 and 0.35 bite/mosquito-day in Thailand and Puerto Rico, respectively).

CONCLUSIONS/SIGNIFICANCE

Our review reinforces the importance of engaging with vector biology when using mosquito biting rate data in transmission modeling studies. For Ae. aegypti, this includes understanding the variation of the gonotrophic cycle duration and the number of bites per gonotrophic cycle, as well as recognizing the potential for spatial and temporal variability. To address these variabilities, we advocate for site-specific data and the development of a standardized approach to estimate the biting rate.

Mathematical modeling of Dengue virus serotypes propagation in Mexico

The Dengue virus (DENV) constitutes a major vector borne virus disease worldwide. Prediction of the DENV spread dynamics, prevalence and infection rates are crucial elements to guide the public health services effort towards meaningful actions. The existence of four DENV serotypes further complicates the virus proliferation forecast. The different serotypes have varying clinical impacts, and the symptomatology of the infection is dependent on the infection history of the patient. Therefore, changes in the prevalent DENV serotype found in one location have a profound impact on the regional public health. The prediction of the spread and intensity of infection of the individual DENV serotypes in specific locations would allow the authorities to plan local pesticide spray to control the vector as well as the purchase of specific antibody therapy. Here we used a mathematical model to predict serotype-specific DENV prevalence and overall case burden in Mexico.

Multiobjective optimization to assess dengue control costs using a climate-dependent epidemiological model

Arboviruses, diseases transmitted by arthropods, have become a significant challenge for public health managers. The World Health Organization highlights dengue as responsible for millions of infections worldwide annually. As there is no specific treatment for the disease and no free-of-charge vaccine for mass use in Brazil, the best option is the measures to combat the vector, the Aedes aegypti mosquito. Therefore, we proposed an epidemiological model dependent on temperature, precipitation, and humidity, considering symptomatic and asymptomatic dengue infections. Through computer simulations, we aimed to minimize the amount of insecticides and the social cost demanded to treat patients. We proposed a case study in which our model is fitted with real data from symptomatic dengue-infected humans in an epidemic year in a Brazilian city. Our multiobjective optimization model considers an additional control using larvicide, adulticide, and ultra-low volume spraying. The work's main contribution is studying the monetary cost of the actions to combat the vector demand versus the hospital cost per confirmed infected, comparing approaches with and without additional control. Results showed that the additional vector control measures are cheaper than the hospital treatment without the vector control would be.

Assessing the impact of serostatus-dependent immunization on mitigating the spread of dengue virus

Dengue is the most rapidly spreading mosquito-borne disease that poses great threats to public health. We propose a compartmental model with primary and secondary infection and targeted vaccination to assess the impact of serostatus-dependent immunization on mitigating the spread of dengue virus. We derive the basic reproduction number and investigate the stability and bifurcations of the disease-free equilibrium and endemic equilibria. The existence of a backward bifurcation is proved and is used to explain the threshold dynamics of the transmission. We also carry out numerical simulations and present bifurcation diagrams to reveal rich dynamics of the model such as bi-stability of the equilibria, limit cycles, and chaos. We prove the uniform persistence and global stability of the model. Sensitivity analysis suggests that mosquito control and protection from mosquito bites are still the key measures of controlling the spread of dengue virus, though serostatus-dependent immunization is implemented. Our findings provide insightful information for public health in mitigating dengue epidemics through vaccination.

Model-based risk assessment of dengue fever transmission in Xiamen City, China

Background

Quantitative assessment of the risk of local transmission from imported dengue cases makes a great challenge to the development of public health in China. The purpose of this study is to observe the risk of mosquito-borne transmission in Xiamen City through ecological and insecticide resistance monitoring. Quantitative evaluation of mosquito insecticide resistance, community population and the number of imported cases affecting the transmission of dengue fever (DF) in Xiamen was carried out based on transmission dynamics model, so as to reveal the correlation between key risk factors and DF transmission.

Methods

Based on the dynamics model and combined with the epidemiological characteristics of DF in Xiamen City, a transmission dynamics model was built to simulate the secondary cases caused by imported cases to evaluate the transmission risk of DF, and to explore the influence of mosquito insecticide resistance, community population and imported cases on the epidemic situation of DF in Xiamen City.

Results

For the transmission model of DF, when the community population is between 10,000 and 25,000, changing the number of imported DF cases and the mortality rate of mosquitoes will have an impact on the spread of indigenous DF cases, however, changing the birth rate of mosquitoes did not gain more effect on the spread of local DF transmission.

Conclusions

Through the quantitative evaluation of the model, this study determined that the mosquito resistance index has an important influence on the local transmission of dengue fever caused by imported cases in Xiamen, and the Brayton index can also affect the local transmission of the disease.

A Systematic Review of Mathematical Models of Dengue Transmission and Vector Control: 2010-2020

Vector control methods are considered effective in averting dengue transmission. However, several factors may modify their impact. Of these controls, chemical methods, in the long run, may increase mosquitoes' resistance to chemicides, thereby decreasing control efficacy. The biological methods, which may be self-sustaining and very effective, could be hampered by seasonality or heatwaves (resulting in, e.g., loss of Wolbachia infection). The environmental methods that could be more effective than the chemical methods are under-investigated. In this study, a systematic review is conducted to explore the present understanding of the effectiveness of vector control approaches via dengue transmission models.

Dynamics of a dengue disease transmission model with two-stage structure in the human population

Age as a risk factor is common in vector-borne infectious diseases. This is partly because children depend on adults to take preventative measures, and adults are less susceptible to mosquito bites because they generally spend less time outdoors than children. We propose a dengue disease model that considers the human population as divided into two subpopulations: children and adults. This is in order to take into consideration that children are more likely than adults to be bitten by mosquitoes. We calculated the basic reproductive number of dengue, using the next-generation operator method. We determined the local and global stability of the disease-free equilibrium. We obtained sufficient conditions for the global asymptotic stability of the endemic equilibrium using the Lyapunov functional method. When the infected periods in children and adults are the same, we that the endemic equilibrium is globally asymptotically stable in the interior of the feasible region when the threshold quantity $ R_0 > 1 $. Additionally, we performed a numerical simulation using parameter values obtained from the literature. Finally, a local sensitivity analysis was performed to identify the parameters that have the greatest influence on changes in $ (R_0) $, and thereby obtain a better biological interpretation of the results.

SIR-SI model with a Gaussian transmission rate: Understanding the dynamics of dengue outbreaks in Lima, Peru

INTRODUCTION

Dengue is transmitted by the Aedes aegypti mosquito as a vector, and a recent outbreak was reported in several districts of Lima, Peru. We conducted a modeling study to explain the transmission dynamics of dengue in three of these districts according to the demographics and climatology.

METHODOLOGY

We used the weekly distribution of dengue cases in the Comas, Lurigancho, and Puente Piedra districts, as well as the temperature data to investigate the transmission dynamics. We used maximum likelihood minimization and the human susceptible-infected-recovered and vector susceptible-infected (SIR-SI) model with a Gaussian function for the infectious rate to consider external non-modeled variables.

RESULTS/PRINCIPAL FINDINGS

We found that the adjusted SIR-SI model with the Gaussian transmission rate (for modelling the exogenous variables) captured the behavior of the dengue outbreak in the selected districts. The model explained that the transmission behavior had a strong dependence on the weather, cultural, and demographic variables while other variables determined the start of the outbreak.

CONCLUSION/SIGNIFICANCE

The experimental results showed good agreement with the data and model results when a Bayesian-Gaussian transmission rate was employed. The effect of weather was also observed, and a strong qualitative relationship was obtained between the transmission rate and computed effective reproduction number Rt.

Modeling the spread of the Zika virus by sexual and mosquito transmission

Zika Virus (ZIKV) is a flavivirus that is transmitted predominantly by the Aedes species of mosquito, but also through sexual contact, blood transfusions, and congenitally from mother to child. Although approximately 80% of ZIKV infections are asymptomatic and typical symptoms are mild, multiple studies have demonstrated a causal link between ZIKV and severe diseases such as Microcephaly and Guillain Barré Syndrome. Two goals of this study are to improve ZIKV models by considering the spread dynamics of ZIKV as both a vector-borne and sexually transmitted disease, and also to approximate the degree of under-reporting. In order to accomplish these objectives, we propose a compartmental model that allows for the analysis of spread dynamics as both a vector-borne and sexually transmitted disease, and fit it to the ZIKV incidence reported to the National System of Public Health Surveillance in 27 municipalities of Colombia between January 1 2015 and December 31 2017. We demonstrate that our model can represent the infection patterns over this time period with high confidence. In addition, we argue that the degree of under-reporting is also well estimated. Using the model we assess potential viability of public health scenarios for mitigating disease spread and find that targeting the sexual pathway alone has negligible impact on overall spread, but if the proportion of risky sexual behavior increases then it may become important. Targeting mosquitoes remains the best approach of those considered. These results may be useful for public health organizations and governments to construct and implement suitable health policies and reduce the impact of the Zika outbreaks.

The Influence of Anthropogenic and Environmental Disturbances on Parameter Estimation of a Dengue Transmission Model

Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010-2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios.

Considering waning immunity to better explain dengue dynamics

Life-long serotype-specific immunity following dengue virus infection may not always occur, but the true extent of this effect is unknown. Analysis of more than 20 years of monotypic epidemics in the isolated French Polynesian islands revealed that whilst the risk of symptomatic dengue infection did conform to the classical paradigms of homotypic immunity and increased disease risk in heterotypic secondary infections, incorporation of waning immunity improved the ability of epidemiological models to capture the observed epidemic dynamics. Not only does this show how inclusion of waning immunity into classical models can reveal important facets of the immune response to natural dengue virus infection, it also has significant ramifications for vaccine development and implementation in dengue endemic areas.

How do i bite thee? let me count the ways: Exploring the implications of individual biting habits of Aedes aegypti for dengue transmission

In models of mosquito-borne transmission, the mosquito biting rate is an influential parameter, and understanding the heterogeneity of the process of biting is important, as biting is usually assumed to be relatively homogeneous across individuals, with time-between-bites described by an exponentially distributed process. However, these assumptions have not been addressed through laboratory experimentation. We experimentally investigated the daily biting habits of Ae. aegypti at three temperatures (24°C, 28°C, and 32°C) and determined that there was individual heterogeneity in biting habits (number of bites, timing of bites, etc.). We further explored the consequences of biting heterogeneity using an individual-based model designed to examine whether a particular biting profile determines whether a mosquito is more or less likely to 1) become exposed given a single index case of dengue (DENV) and 2) transmit to a susceptible human individual. Our experimental results indicate that there is heterogeneity among individuals and among temperature treatments. We further show that this results in altered probabilities of transmission of DENV to and from individual mosquitoes based on biting profiles. While current model representation of biting may work under some conditions, it might not uniformly be the best fit for this process. Our data also confirm that biting is a non-monotonic process with temperatures around 28°C being optimum.

Epidemiological Characteristics and the Dynamic Transmission Model of Dengue Fever in Zhanjiang City, Guangdong Province in 2018

Background: With the progress of urbanization, the mobility of people has gradually increased, which has led to the further spread of dengue fever. This study evaluated the transmissibility of dengue fever within districts and between different districts in Zhanjiang City to provide corresponding advice for cross-regional prevention and control. Methods: A mathematical model of transmission dynamics was developed to explore the transmissibility of the disease and to compare that between different regions. Results: A total of 467 DF cases (6.38 per 100,000 people) were reported in Zhanjiang City in 2018. In the model, without any intervention, the number of simulated cases in this epidemic reached about 950. The dengue fever transmissions between districts varied within and between regions. When the spread of dengue fever from Chikan Districts to other districts was cut off, the number of cases in other districts dropped significantly or even to zero. When the density of mosquitoes in Xiashan District was controlled, the dengue fever epidemic in Xiashan District was found to be significantly alleviated. Conclusions: When there is a dengue outbreak, timely measures can effectively control it from developing into an epidemic. Different prevention and control measures in different districts could efficiently reduce the risk of disease transmission.

Mathematical modeling in perspective of vector-borne viral infections: a review

Background

Viral diseases are highly widespread infections caused by viruses. These viruses are passing from one human to other humans through a certain medium. The medium might be mosquito, animal, reservoir and food, etc. Here, the population of both human and mosquito vectors are important.

Main body of the abstract

The main objectives are here to introduce the historical perspective of mathematical modeling, enable the mathematical modeler to understand the basic mathematical theory behind this and present a systematic review on mathematical modeling for four vector-borne viral diseases using the deterministic approach. Furthermore, we also introduced other mathematical techniques to deal with vector-borne diseases. Mathematical models could help forecast the infectious population of humans and vectors during the outbreak.

Short conclusion

This study will be helpful for mathematical modelers in vector-borne diseases and ready-made material in the review for future advancement in the subject. This study will not only benefit vector-borne conditions but will enable ideas for other illnesses.

Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches

The COVID-19 pandemic has followed a wave pattern, with an increase in new cases followed by a drop. Several factors influence this pattern, including vaccination efficacy over time, human behavior, infection management measures used, emergence of novel variants of SARS-CoV-2, and the size of the vulnerable population, among others. In this study, we used three statistical approaches to analyze COVID-19 dissemination data collected from 15 November 2021 to 09 January 2022 for the prediction of further spread and to determine the behavior of the pandemic in the top 12 countries by infection incidence at that time, namely Distribution Fitting, Time Series Modeling, and Epidemiological Modeling. We fitted various theoretical distributions to data sets from different countries, yielding the best-fit distribution for the most accurate interpretation and prediction of the disease spread. Several time series models were fitted to the data of the studied countries using the expert modeler to obtain the best fitting models. Finally, we estimated the infection rates (β), recovery rates (γ), and Basic Reproduction Numbers ([Formula: see text]) for the countries using the compartmental model SIR (Susceptible-Infectious-Recovered). Following more research on this, our findings may be validated and interpreted. Therefore, the most refined information may be used to develop the best policies for breaking the disease's chain of transmission by implementing suppressive measures such as vaccination, which will also aid in the prevention of future waves of infection.

Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009–2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreak are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well in the last months of the year when isolated cases appear outside the outbreak periods, probably due to non- climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.

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A SIR Leptospirosis model considering hydroclimatic variables is proposed.

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The model is implemented in 3 important cities of Argentina, for the 2009–2018 period.

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Results are in good agreement with the registered data for a leptospirosis outbreak.

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The tool could constitute a climatic service for the public health system.

Mathematical models for dengue fever epidemiology: A 10-year systematic review

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.

Game-Theoretical Model of the Voluntary Use of Insect Repellents to Prevent Zika Fever

Zika fever is an emerging mosquito-borne disease. While it often causes no or only mild symptoms that are similar to dengue fever, Zika virus can spread from a pregnant woman to her baby and cause severe birth defects. There is no specific treatment or vaccine, but the disease can be mitigated by using several control strategies, generally focusing on the reduction in mosquitoes or mosquito bites. In this paper, we model Zika virus transmission and incorporate a game-theoretical approach to study a repeated population game of DEET usage to prevent insect bites. We show that the optimal use effectively leads to disease elimination. This result is robust and not significantly dependent on the cost of the insect repellents.

Analysis of yellow fever prevention strategy from the perspective of mathematical model and cost-effectiveness analysis

We developed a new mathematical model for yellow fever under three types of intervention strategies: vaccination, hospitalization, and fumigation. Additionally, the side effects of the yellow fever vaccine were also considered in our model. To analyze the best intervention strategies, we constructed our model as an optimal control model. The stability of the equilibrium points and basic reproduction number of the model are presented. Our model indicates that when yellow fever becomes endemic or disappears from the population, it depends on the value of the basic reproduction number, whether it larger or smaller than one. Using the Pontryagin maximum principle, we characterized our optimal control problem. From numerical experiments, we show that the optimal levels of each control must be justified, depending on the strategies chosen to optimally control the spread of yellow fever.

Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

Background

Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics.

Results

We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever.

Conclusions

Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

A Zika Endemic Model for the Contribution of Multiple Transmission Routes

Zika virus disease is a viral disease primarily transmitted to humans through the bite of infected female mosquitoes. Recent evidence indicates that the virus can also be sexually transmitted in hosts and vertically transmitted in vectors. In this paper, we propose a Zika model with three transmission routes, that is, vector-borne transmission between humans and mosquitoes, sexual transmission within humans and vertical transmission within mosquitoes. The basic reproduction number [Formula: see text] is computed and shown to be a sharp threshold quantity. Namely, the disease-free equilibrium is globally asymptotically stable as [Formula: see text], whereas there exists a unique endemic equilibrium which is globally asymptotically stable as [Formula: see text]. The relative contributions of each transmission route on the reproduction number, and the short- and long-term host infections are analyzed. Numerical simulations confirm that vectorial transmission contributes the most to the initial and subsequent transmission. The role of sexual transmission in the early phase of a Zika outbreak is greater than the long term, while vertical transmission is the opposite. Reducing mosquito bites is the most effective measure in lowering the risk of Zika virus infection.

Dengue models based on machine learning techniques: A systematic literature review

BACKGROUND

Dengue modeling is a research topic that has increased in recent years. Early prediction and decision-making are key factors to control dengue. This Systematic Literature Review (SLR) analyzes three modeling approaches of dengue: diagnostic, epidemic, intervention. These approaches require models of prediction, prescription and optimization. This SLR establishes the state-of-the-art in dengue modeling, using machine learning, in the last years.

METHODS

Several databases were selected to search the articles. The selection was made based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Sixty-four articles were obtained and analyzed to describe their strengths and limitations. Finally, challenges and opportunities for research on machine-learning for dengue modeling were identified.

RESULTS

Logistic regression was the most used modeling approach for the diagnosis of dengue (59.1%). The analysis of the epidemic approach showed that linear regression (17.4%) is the most used technique within the spatial analysis. Finally, the most used intervention modeling is General Linear Model with 70%.

CONCLUSIONS

We conclude that cause-effect models may improve diagnosis and understanding of dengue. Models that manage uncertainty can also be helpful, because of low data-quality in healthcare. Finally, decentralization of data, using federated learning, may decrease computational costs and allow model building without compromising data security.

A Framework for Weather-Driven Dengue Virus Transmission Dynamics in Different Brazilian Regions

This study investigated a model to assess the role of climate fluctuations on dengue (DENV) dynamics from 2010 to 2019 in four Brazilian municipalities. The proposed transmission model was based on a preexisting SEI-SIR model, but also incorporates the vector vertical transmission and the vector's egg compartment, thus allowing rainfall to be introduced to modulate egg-hatching. Temperature and rainfall satellite data throughout the decade were used as climatic model inputs. A sensitivity analysis was performed to understand the role of each parameter. The model-simulated scenario was compared to the observed dengue incidence and the findings indicate that the model was able to capture the observed seasonal dengue incidence pattern with good accuracy until 2016, although higher deviations were observed from 2016 to 2019. The results further demonstrate that vertical transmission fluctuations can affect attack transmission rates and patterns, suggesting the need to investigate the contribution of vertical transmission to dengue transmission dynamics in future assessments. The improved understanding of the relationship between different environment variables and dengue transmission achieved by the proposed model can contribute to public health policies regarding mosquito-borne diseases.

Modeling, analysis and prediction of new variants of covid-19 and dengue co-infection on complex network

Recently, four new strains of SARS-COV-2 were reported in different countries which are mutants and considered as 70 % more dangerous than the existing covid-19 virus. In this paper, hybrid mathematical models of new strains and co-infection in Caputo, Caputo-Fabrizio, and Atangana-Baleanu are presented. The idea behind this co-infection modeling is that, as per medical reports, both dengue and covid-19 have similar symptoms at the early stages. Our aim is to evaluate and predict the transmission dynamics of both deadly viruses. The qualitative study via stability analysis is discussed at equilibria and reproduction number R 0 is computed. For the numerical purpose, Adams-Bashforth-Moulton and Newton methods are employed to obtain the approximate solutions of the proposed model. Sensitivity analysis is carried out to assessed the effects of various biological parameters and rates of transmission on the dynamics of both viruses. We also compared our results with some reported data against infected, recovered, and death cases.

A Study of Within-Host Dynamics of Dengue Infection incorporating both Humoral and Cellular Response with a Time Delay for Production of Antibodies

a. Background: Dengue is an acute illness caused by a virus. The complex behaviour of the virus in human body can be captured using mathematical models. These models helps us to enhance our understanding on the dynamics of the virus.

b. Objectives: We propose to study the dynamics of within-host epidemic model of dengue infection which incorporates both innate immune response and adaptive immune response (Cellular and Humoral). The proposed model also incorporates the time delay for production of antibodies from B cells. We propose to understand the dynamics of the this model using the dynamical systems approach by performing the stability and sensitivity analysis.

c. Methods used: The basic reproduction number (R0) has been computed using the next generation matrix method. The standard stability analysis and sensitivity analysis were performed on the proposed model.

d. Results: The critical level of the antibody recruitment rate(q) was found to be responsible for the existence and stability of various steady states. The stability of endemic state was found to be dependent on time delay(τ). The sensitivity analysis identified the production rate of antibodies (q) to be highly sensitive parameter.

e. Conclusions: The existence and stability conditions for the equilibrium points have been obtained. The threshold value of time delay (τ0) has been computed which is critical for change in stability of the endemic state. Sensitivity analysis was performed to identify the crucial and sensitive parameters of the model.

Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.

Threshold Dynamics in a Model for Zika Virus Disease with Seasonality

We present a compartmental population model for the spread of Zika virus disease including sexual and vectorial transmission as well as asymptomatic carriers. We apply a non-autonomous model with time-dependent mosquito birth, death and biting rates to integrate the impact of the periodicity of weather on the spread of Zika. We define the basic reproduction number [Formula: see text] as the spectral radius of a linear integral operator and show that the global dynamics is determined by this threshold parameter: If [Formula: see text] then the disease-free periodic solution is globally asymptotically stable, while if [Formula: see text] then the disease persists. We show numerical examples to study what kind of parameter changes might lead to a periodic recurrence of Zika.

Recent African strains of Zika virus display higher transmissibility and fetal pathogenicity than Asian strains

The global emergence of Zika virus (ZIKV) revealed the unprecedented ability for a mosquito-borne virus to cause congenital birth defects. A puzzling aspect of ZIKV emergence is that all human outbreaks and birth defects to date have been exclusively associated with the Asian ZIKV lineage, despite a growing body of laboratory evidence pointing towards higher transmissibility and pathogenicity of the African ZIKV lineage. Whether this apparent paradox reflects the use of relatively old African ZIKV strains in most laboratory studies is unclear. Here, we experimentally compare seven low-passage ZIKV strains representing the recently circulating viral genetic diversity. We find that recent African ZIKV strains display higher transmissibility in mosquitoes and higher lethality in both adult and fetal mice than their Asian counterparts. We emphasize the high epidemic potential of African ZIKV strains and suggest that they could more easily go unnoticed by public health surveillance systems than Asian strains due to their propensity to cause fetal loss rather than birth defects.

Here, the authors compare seven low passage Zika virus (ZIKV) strains representing the recently circulating viral genetic diversity of African and Asian strains and find that African ZIKV strains have higher transmissibility in mosquitoes and higher lethality in both adult and fetal mice.

Effect of daily human movement on some characteristics of dengue dynamics

Human movement is a key factor in infectious diseases spread such as dengue. Here, we explore a mathematical modeling approach based on a system of ordinary differential equations to study the effect of human movement on characteristics of dengue dynamics such as the existence of endemic equilibria, and the start, duration, and amplitude of the outbreak. The model considers that every day is divided into two periods: high-activity and low-activity. Periodic human movement between patches occurs in discrete times. Based on numerical simulations, we show unexpected scenarios such as the disease extinction in regions where the local basic reproductive number is greater than 1. In the same way, we obtain scenarios where outbreaks appear despite the fact that the local basic reproductive numbers in these regions are less than 1 and the outbreak size depends on the length of high-activity and low-activity periods.

Dengue fever transmission between a construction site and its surrounding communities in China

BACKGROUND

Due to an increase in mosquito habitats and the lack facilities to carry out basic mosquito control, construction sites in China are more likely to experience secondary dengue fever infection after importation of an initial infection, which may then increase the number of infections in the neighboring communities and the chance of community transmission. The aim of this study was to investigate how to effectively reduce the transmission of dengue fever at construction sites and the neighboring communities.

METHODS

The Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model of human and SEI model of mosquitoes were developed to estimate the transmission of dengue virus between humans and mosquitoes within the construction site and within a neighboring community, as well between each of these. With the calibrated model, we further estimated the effectiveness of different intervention scenarios targeting at reducing the transmissibility at different locations (i.e. construction sites and community) with the total attack rate (TAR) and the duration of the outbreak (DO).

RESULTS

A total of 102 construction site-related and 131 community-related cases of dengue fever were reported in our area of study. Without intervention, the number of cases related to the construction site and the community rose to 156 (TAR: 31.25%) and 10,796 (TAR: 21.59%), respectively. When the transmission route from mosquitoes to humans in the community was cut off, the number of community cases decreased to a minimum of 33 compared with other simulated scenarios (TAR: 0.068%, DO: 60 days). If the transmission route from infectious mosquitoes in the community and that from the construction site to susceptible people on the site were cut off at the same time, the number of cases on the construction site dropped to a minimum of 74 (TAR: 14.88%, DO: 66 days).

CONCLUSIONS

To control the outbreak of dengue fever effectively on both the construction site and in the community, interventions needed to be made both within the community and from the community to the construction site. If interventions only took place within the construction site, the number of cases on the construction site would not be reduced. Also, interventions implemented only within the construction site or between the construction site and the community would not lead to a reduction in the number of cases in the community.

The effect of demographic and environmental variability on disease outbreak for a dengue model with a seasonally varying vector population

Seasonal changes in temperature, humidity, and rainfall affect vector survival and emergence of mosquitoes and thus impact the dynamics of vector-borne disease outbreaks. Recent studies of deterministic and stochastic epidemic models with periodic environments have shown that the average basic reproduction number is not sufficient to predict an outbreak. We extend these studies to time-nonhomogeneous stochastic dengue models with demographic variability wherein the adult vectors emerge from the larval stage vary periodically. The combined effects of variability and periodicity provide a better understanding of the risk of dengue outbreaks. A multitype branching process approximation of the stochastic dengue model near the disease-free periodic solution is used to calculate the probability of a disease outbreak. The approximation follows from the solution of a system of differential equations derived from the backward Kolmogorov differential equation. This approximation shows that the risk of a disease outbreak is also periodic and depends on the particular time and the number of the initial infected individuals. Numerical examples are explored to demonstrate that the estimates of the probability of an outbreak from that of branching process approximations agree well with that of the continuous-time Markov chain. In addition, we propose a simple stochastic model to account for the effects of environmental variability on the emergence of adult vectors from the larval stage.

Age-dependent seroprevalence of dengue and chikungunya: inference from a cross-sectional analysis in Esmeraldas Province in coastal Ecuador

OBJECTIVES

There are few population-based estimates for prevalence of past exposure to dengue and chikungunya viruses despite common epidemiological features. Here, we have developed a novel statistical method to study patterns of age-dependent prevalence of immunity in a population following exposures to two viruses which share similar epidemiological features including mode of transmission and induction of long-lasting immunity. This statistical technique accounted for sociodemographic characteristics associated with individuals and households.

SETTINGS

The data consist of a representative sample from an ongoing longitudinal birth cohort set-up in a tropical district in coastal Ecuador (Esmeraldas).

PARTICIPANTS

We collected data and blood samples from 319 individuals belonging to 152 households following epidemics of the infections in 2015 in Latin America.

PRIMARY OUTCOME

Plasma was tested for the presence of specific IgG antibodies to dengue and chikungunya viruses by commercial ELISA and defined a bivariate binary outcome indicating individuals' past exposure status to dengue and chikungunya (ie, presence/absence of IgG antibodies to dengue or chikungunya or both).

RESULTS

Dengue seroprevalence increased rapidly with age reaching 97% (95% credible interval (CrI): 93%-99%) by 60 years. Chikungunya seroprevalence peaked at 42% (95% CrI: 18%-66%) around 9 years of age and averaged 27% (95% CrI: 8.7%-51.6%) for all ages. Rural areas were more likely to be associated with dengue-only exposure while urban areas and shorter distance to the nearest household were associated with exposures to both. Women living in urban settings were more likely to be chikungunya seropositive while rural men were more likely to be dengue seropositive.

CONCLUSION

Dengue seroprevalence was strongly age dependent consistent with endemic exposure while that of chikungunya peaked in childhood consistent with the recent emergence of the virus in the study area. Our findings will inform control strategies for the two arboviruses in Ecuador including recommendations by the WHO on dengue vaccination.

Simulation models of dengue transmission in Funchal, Madeira Island: Influence of seasonality

The recent emergence and established presence of Aedes aegypti in the Autonomous Region of Madeira, Portugal, was responsible for the first autochthonous outbreak of dengue in Europe. The island has not reported any dengue cases since the outbreak in 2012. However, there is a high risk that an introduction of the virus would result in another autochthonous outbreak given the presence of the vector and permissive environmental conditions. Understanding the dynamics of a potential epidemic is critical for targeted local control strategies. Here, we adopt a deterministic model for the transmission of dengue in Aedes aegypti mosquitoes. The model integrates empirical and mechanistic parameters for virus transmission, under seasonally varying temperatures for Funchal, Madeira Island. We examine the epidemic dynamics as triggered by the arrival date of an infectious individual; the influence of seasonal temperature mean and variation on the epidemic dynamics; and performed a sensitivity analysis on the following quantities of interest: the epidemic peak size, time to peak, and the final epidemic size. Our results demonstrate the potential for summer and autumn season transmission of dengue, with the arrival date significantly affecting the distribution of the timing and peak size of the epidemic. Late-summer arrivals were more likely to produce large epidemics within a short peak time. Epidemics within this favorable period had an average of 11% of the susceptible population infected at the peak, at an average peak time of 95 days. We also demonstrated that seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with a short peak time and vice versa. Overall, our quantities of interest were most sensitive to variance in the date of arrival, seasonal temperature, transmission rates, mortality rate, and the mosquito population; the magnitude of sensitivity differs across quantities. Our model could serve as a useful guide in the development of effective local control and mitigation strategies for dengue fever in Madeira Island.

Impact of sars-cov-2 interventions on dengue transmission

An estimated 105 million dengue infections occur per year across 120 countries, where traditional vector control is the primary control strategy to reduce contact between mosquito vectors and people. The ongoing sars-cov-2 pandemic has resulted in dramatic reductions in human mobility due to social distancing measures; the effects on vector-borne illnesses are not known. Here we examine the pre and post differences of dengue case counts in Malaysia, Singapore and Thailand, and estimate the effects of social distancing as a treatment effect whilst adjusting for temporal confounders. We found that social distancing is expected to lead to 4.32 additional cases per 100,000 individuals in Thailand per month, which equates to 170 more cases per month in the Bangkok province (95% CI: 100-242) and 2008 cases in the country as a whole (95% CI: 1170-2846). Social distancing policy estimates for Thailand were also found to be robust to model misspecification, and variable addition and omission. Conversely, no significant impact on dengue transmission was found in Singapore or Malaysia. Across country disparities in social distancing policy effects on reported dengue cases are reasoned to be driven by differences in workplace-residence structure, with an increase in transmission risk of arboviruses from social distancing primarily through heightened exposure to vectors in elevated time spent at residences, demonstrating the need to understand the effects of location on dengue transmission risk under novel population mixing conditions such as those under social distancing policies.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

MODELING ZIKA TRANSMISSION DYNAMICS: PREVENTION AND CONTROL

The Zika virus (ZIKV) epidemic is depicted to have high spatial diversity and slow growth, attributable to the dynamics of the mosquito vector and mobility of the human populations. In an effort to understand the transmission dynamics of Zika virus, we formulate a new compartmental epidemic model with a system of seven differential equations and 11 parameters incorporating the decaying transmission rate and study the impact of protection measure on basic public health. We do not fit the model to the observed pattern of spread, rather we use parameter values estimated in the past and examine the extent to which the designed model prediction agrees with the pattern of spread seen in Brazil, via reaction–diffusion modeling. Our work includes estimation of key epidemiological parameters such as basic reproduction number ([Formula: see text], and gives a rough estimate of how many individuals can be typically infected during an outbreak if it occurs in India. We used partial rank correlation coefficient method for global sensitivity analysis to identify the most influential model parameters. Using optimal control theory and Pontryagin’s maximum principle, a control model has been proposed and conditions for the optimal control are determined for the deterministic model of Zika virus. The control functions for the strategies (i) vector-to-human contact reduction and (ii) vector elimination are introduced into the system. Numerical simulations are also performed. This work aimed at understanding the potential extent and timing of the ZIKV epidemic can be used as a template for the analysis of future mosquito-borne epidemics.