Forcing versus feedback: epidemic malaria and monsoon rains in northwest India

Informing policy via dynamic models: Cholera in Haiti

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

Possible potential spread of Anopheles stephensi, the Asian malaria vector

BACKGROUND

Anopheles stephensi is native to Southeast Asia and the Arabian Peninsula and has emerged as an effective and invasive malaria vector. Since invasion was reported in Djibouti in 2012, the global invasion range of An. stephensi has been expanding, and its high adaptability to the environment and the ongoing development of drug resistance have created new challenges for malaria control. Climate change is an important factor affecting the distribution and transfer of species, and understanding the distribution of An. stephensi is an important part of malaria control measures, including vector control.

METHODS

In this study, we collected existing distribution data for An. stephensi, and based on the SSP1-2.6 future climate data, we used the Biomod2 package in R Studio through the use of multiple different model methods such as maximum entropy models (MAXENT) and random forest (RF) in this study to map the predicted global An. stephensi climatically suitable areas.

RESULTS

According to the predictions of this study, some areas where there are no current records of An. stephensi, showed significant areas of climatically suitable for An. stephensi. In addition, the global climatically suitability areas for An. stephensi are expanding with global climate change, with some areas changing from unsuitable to suitable, suggesting a greater risk of invasion of An. stephensi in these areas, with the attendant possibility of a resurgence of malaria, as has been the case in Djibouti.

CONCLUSIONS

This study provides evidence for the possible invasion and expansion of An. stephensi and serves as a reference for the optimization of targeted monitoring and control strategies for this malaria vector in potential invasion risk areas.

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera-Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Malaria, climate variability, and interventions: modelling transmission dynamics

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.

Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission

Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model’s ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda’s diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.

Climate plays a fundamental and complex role in malaria transmission, by acting on multiple aspects of mosquito ecology and parasite transmissibility. However, to express malaria transmission pressure, malaria models with implicit vector dynamics have relied on indirect predictors of vector ecology, such as temporal seasonality or interpolations of rainfall/temperature, instead of entomological processes directly informed by ambient conditions. This approach obscures the specific influence of environmental conditions on relevant vector and parasite ecology, as well as meaningful interpretation of climate variability within these models. Here, we demonstrate that both interpretability and ecological effect from climate can be instantiated in lower dimension dynamical models through representation of transmission pressures via a climate-driven Entomological Inoculation Rate (EIR). This process-based model framework is driven by local rainfall and temperature, which regulate multiple aspects of the EIR, namely mosquito density, host-seeking activity, and parasite infectivity. Our results indicate that the climate-driven model construct is able to reproduce regional and local malaria transmission at seasonal and inter-annual time scales, while enabling identification of key entomological determinants of transmission.

Spatio-temporal variability of malaria infection in Chahbahar County, Iran: association with the ENSO and rainfall variability

Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.

Assessment of Climate-Driven Variations in Malaria Transmission in Senegal Using the VECTRI Model

Several vector-borne diseases, such as malaria, are sensitive to climate and weather conditions. When unusual conditions prevail, for example, during periods of heavy rainfall, mosquito populations can multiply and trigger epidemics. This study, which consists of better understanding the link between malaria transmission and climate factors at a national level, aims to validate the VECTRI model (VECtor borne disease community model of ICTP, TRIeste) in Senegal. The VECTRI model is a grid-distributed dynamical model that couples a biological model for the vector and parasite life cycles to a simple compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) representation of the disease progression in the human host. In this study, a VECTRI model driven by reanalysis data (ERA-5) was used to simulate malaria parameters, such as the entomological inoculation rate (EIR) in Senegal. Observed malaria data from the National Malaria Control Program in Senegal (PNLP/Programme National de Lutte contre le Paludisme au Senegal) and outputs from the climate data used in this study were compared. The findings highlight the unimodal shape of temporal malaria occurrence, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall, showing a south–north gradient over Senegal. This study showed that the peak of malaria takes place from September to October, with a lag of about one month from the peak of rainfall in Senegal. There is an agreement between observations and simulations about decreasing malaria cases on time. These results indicate that the southern area of Senegal is at the highest risk of malaria spread outbreaks. The findings in the paper are expected to guide community-based early-warning systems and adaptation strategies in Senegal, which will feed into the national malaria prevention, response, and care strategies adapted to the needs of local communities.

Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.

This study shows that spatially large-scale shifts in temperature can synchronize dengue dynamics across Thailand; however, as average temperatures rise, dengue dynamics may increasingly be dictated by dynamics of immunity, which may in turn mean fewer synchronous outbreaks in the future.

Malaria elimination on Hainan Island despite climate change

Background

Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear.

Methods

Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes.

Results

Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%–9.6%).

Conclusions

Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.

Several malaria control strategies have been implemented on Hainan Island, China, and it is important to determine which of these have been effective to guide future efforts to control malaria. Here, we use mathematical and statistical methods to assess the effectiveness of control methods using data on malaria cases on Hainan, considering the impact of climate change simultaneously, since malaria transmission is affected by the climate. We observe time-related trends in malaria incidence and a strong relationship with climate before the large-scale rollout of malaria control interventions. We find that insecticide-treated bed nets are the most effective strategy in decreasing malaria incidence, while mass drug administration and indoor residual spraying also contribute to malaria control. Our findings provide evidence that a combination of strategies reduces the burden of malaria in affected regions.

Tian et al. use mathematical modelling to estimate the impact of various interventions on malaria incidence on Hainan Island, also taking into account climate change. They find that although malaria transmission has been exacerbated by climate change, insecticide-treated bed nets and other interventions were effective in controlling the disease.

The neglected role of relative humidity in the interannual variability of urban malaria in Indian cities

The rapid pace of urbanization makes it imperative that we better understand the influence of climate forcing on urban malaria transmission. Despite extensive study of temperature effects in vector-borne infections in general, consideration of relative humidity remains limited. With process-based dynamical models informed by almost two decades of monthly surveillance data, we address the role of relative humidity in the interannual variability of epidemic malaria in two semi-arid cities of India. We show a strong and significant effect of humidity during the pre-transmission season on malaria burden in coastal Surat and more arid inland Ahmedabad. Simulations of the climate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospectively capture the observed variability of disease incidence, and also prospectively predict that of 'out-of-fit' cases in more recent years, with high accuracy. Our findings indicate that relative humidity is a critical factor in the spread of urban malaria and potentially other vector-borne epidemics, and that climate change and lack of hydrological planning in cities might jeopardize malaria elimination efforts.

The association between gold mining and malaria in Guyana: a statistical inference and time-series analysis

BACKGROUND

Guyana reported a significant rise in malaria between 2008 and 2014. As there was no evidence of impairment of national malaria control strategies, public health authorities attributed the surge to a temporal increase in gold mining activity in forested regions. However, systematic analysis of this association is lacking because of the difficulties associated with collecting reliable data for both malaria and mining. We aimed to investigate the association between the international gold price and Plasmodium falciparum malaria transmission in Guyana between 2007 and 2019. We also aimed to evaluate the association between P falciparum cases and the El Niño-Southern Oscillation pattern, which has previously been suggested as a major driver of malaria.

METHODS

We used national malaria surveillance data from Guyana to estimate the correlation over time between the international gold price and reported P falciparum infections in individuals who were likely to be involved in mining activities (ie, men and boys aged between 15 and 50 years who were living in mining regions) for each month between 2007 and 2019. We compared the estimates with those obtained from individuals who were unlikely to be directly involved in mining activities (ie, women, children aged 12 years and younger, and adults aged over 70 years) and estimates obtained from individuals living in non-mining regions. We also evaluated the correlation between P falciparum infections and the El Niño-Southern Oscillation pattern in the same subpopulations and time period. Lastly, we evaluated the performance of a statistical model formulated to estimate P falciparum infections in real time using the international gold price as the predictor variable.

FINDINGS

The proportion of P falciparum malaria cases in temporary residents, which was used as a proxy for circulating individuals involved in gold mining, was highest during the years of peak gold price (ie, between 2008 and 2014). Cases of malaria in all demographic groups showed a strong positive correlation with the gold price, but only in regions with mining camps (0·88 [95% CI 0·84-0·89] for boys and men aged between 15 and 50 years and 0·80 [0·73-0·85] for the aggregated population of women, children aged 12 years and younger, and adults older than 70 years). The highest correlation occurred earlier in men and boys aged between 15 and 50 years, the demographic most likely to be miners, suggesting that transmission in mining camps is followed by infections in the community. On the basis of these findings, we were able to reliably forecast P falciparum malaria trends using only the gold price as the predictor variable. A 1% increase in gold price was associated with a 2·13% increase in P falciparum infections after 1 month in the mining populations, and with a 1·63% increase after 2 months in the non-mining populations. Lastly, La Niña climatic events showed an additional, smaller positive correlation with malaria transmission.

INTERPRETATION

Our analysis provides evidence that the P falciparum malaria surge observed in Guyana between 2008 and 2014 was likely to have been driven mainly by an increase in gold mining, while climate factors might have contributed synergistically. We propose that the international gold price over time is a useful indicator of malaria trends. We conclude that the feasibility of malaria elimination in Guyana, and in other areas in the Amazon where malaria and gold mining overlap, should be evaluated against the challenges posed by rapidly rising gold prices.

FUNDING

Ramón Areces Foundation, National Institutes of Health, and National Institute of General Medical Sciences.

Impact of an accelerated melting of Greenland on malaria distribution over Africa

Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.

The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

Malaria trends in Ethiopian highlands track the 2000 'slowdown' in global warming

A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.

Implications of monsoon season and UVB radiation for COVID-19 in India

India has recorded 142,186 deaths over 36 administrative regions placing India third in the world after the US and Brazil for COVID-19 deaths as of 12 December 2020. Studies indicate that south-west monsoon season plays a role in the dynamics of contagious diseases, which tend to peak post-monsoon season. Recent studies show that vitamin D and its primary source Ultraviolet-B (UVB) radiation may play a protective role in mitigating COVID-19 deaths. However, the combined roles of the monsoon season and UVB radiation in COVID-19 in India remain still unclear. In this observational study, we empirically study the respective roles of monsoon season and UVB radiation, whilst further exploring, whether the monsoon season negatively impacts the protective role of UVB radiation in COVID-19 deaths in India. We use a log-linear Mundlak model to a panel dataset of 36 administrative regions in India from 14 March 2020-19 November 2020 (n = 6751). We use the cumulative COVID-19 deaths as the dependent variable. We isolate the association of monsoon season and UVB radiation as measured by Ultraviolet Index (UVI) from other confounding time-constant and time-varying region-specific factors. After controlling for various confounding factors, we observe that a unit increase in UVI and the monsoon season are separately associated with 1.2 percentage points and 7.5 percentage points decline in growth rates of COVID-19 deaths in the long run. These associations translate into substantial relative changes. For example, a permanent unit increase of UVI is associated with a decrease of growth rates of COVID-19 deaths by 33% (= - 1.2 percentage points) However, the monsoon season, mitigates the protective role of UVI by 77% (0.92 percentage points). Our results indicate a protective role of UVB radiation in mitigating COVID-19 deaths in India. Furthermore, we find evidence that the monsoon season is associated with a significant reduction in the protective role of UVB radiation. Our study outlines the roles of the monsoon season and UVB radiation in COVID-19 in India and supports health-related policy decision making in India.

Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax in French Guiana: 2005-2019

Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 2005–2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box–Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis—PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l’Oyapock and Antecume Pata for P. falciparum, and Saint Georges de l’Oyapock, Antecume Pata, Régina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.

Models with environmental drivers offer a plausible mechanism for the rapid spread of infectious disease outbreaks in marine organisms

The first signs of sea star wasting disease (SSWD) epidemic occurred in just few months in 2013 along the entire North American Pacific coast. Disease dynamics did not manifest as the typical travelling wave of reaction-diffusion epidemiological model, suggesting that other environmental factors might have played some role. To help explore how external factors might trigger disease, we built a coupled oceanographic-epidemiological model and contrasted three hypotheses on the influence of temperature on disease transmission and pathogenicity. Models that linked mortality to sea surface temperature gave patterns more consistent with observed data on sea star wasting disease, which suggests that environmental stress could explain why some marine diseases seem to spread so fast and have region-wide impacts on host populations.

Stochasticity and Infectious Disease Dynamics: Density and Weather Effects on a Fungal Insect Pathogen

In deterministic models of epidemics, there is a host abundance threshold above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, Entomophaga maimaiga, that infects the gypsy moth, Lymantria dispar. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on E. maimaiga epizootics (epidemics in animals). We then used Akaike information criterion analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects or only for density-dependent effects do a poor job of explaining the data. Density-dependent transmission in our best model produces a host density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.

Determining the cutoff of rainfall for Plasmodium falciparum malaria outbreaks in India

BACKGROUND

Malaria outbreaks due to Plasmodium falciparum have been reported from various parts of India. Rainfall is considered as one of the major determinants for malaria outbreaks, however, an estimate of rainfall threshold for malaria is not known. Owing to the vast geographic area, the present study was planned to determine the amount of rainfall required for malaria outbreaks and the lag period between outbreak and rainfall in different Indian climatic regions.

METHODS

Simple statistical methods of overall mean and moving mean (case/mean ratio, CMR) were used to identify the districts with P. falciparum malaria outbreak due to rainfall and the amount of rainfall required for outbreaks. Of 120 districts reporting P. falciparum malaria outbreaks, 99 districts having substantially low number of cases (<100); <3 CMR in any year from 2009 to 2012 and districts having stable malaria transmission were excluded. Finally, analysis of outbreak month, lag period and the threshold of rainfall were determined in respect of 21 districts which represent different agro-climatic zones in the country. Whenever the moving mean of cases attained the value ≥3 in any month, that month was identified as P. falciparum malaria outbreak. The threshold amount of rainfall critical for malaria outbreak was calculated by the average mean of previous months (i.e., lag period).

RESULTS

The rainfall cutoff ranged from >70 to >600mm in different districts. The month of outbreak varied with the climatic zones viz. arid, semi-arid, humid and per-humid districts. In humid and per-humid districts, outbreaks occurred during monsoon period whereas in arid and semi-arid regions outbreaks occurred during the post monsoon period. The lag period varied from 1-3 months; long lag period was observed in arid, semi-arid region, while short lag period in humid and per-humid regions.

CONCLUSION

Malaria outbreaks can occur in post monsoon in arid and semi-arid region whereas in summer/monsoon in humid and per-humid regions. The lag period between rainfall and outbreak differs from one month with short lag (humid and per-humid) to long lag (arid and semi-arid). Based on the determined threshold of rainfall, the findings would be helpful in forewarning of imminent outbreak of malaria for the timely preparedness of malaria outbreaks not only in 21 studied districts, but also in several other districts of different climatic regions of India.

Estimation of the reproduction number and identification of periodicity for HFMD infections in northwest China

Repeated outbreaks of Hand, foot and mouth disease (HFMD) infections have been observed in recent decades and dominated by various enteroviral serotypes. In particular, enterovirus 71 (EV-A71), coxsackievirus A16 (CV-A16) and coxsackievirus A6 (CV-A6) dominated the prevalence of HFMD infections alternatively in recent years with various outbreak sizes in Baoji, a city of Shaanxi Province in Northwest China. Estimating the reproduction number for various enteroviruses serotypes in northwest China (north temperate zone) and identification of cyclicity of HFMD infections are therefore an issue of great importance for future epidemics prediction and control. The basic/effective reproduction numbers for EV-A71, CV-A16 and CV-A6 were estimated based on daily new cases in 2010, 2011 and 2018, respectively, in which the corresponding pathogen dominated the epidemic. Two different methods based on serial interval were adopted and the basic reproduction number were estimated to be in the range of (1.33, 1.46) for CV-A16, (1.20, 1.29) for EV-A71, and (1.38, 1.59) for CV-A6, respectively. The estimated daily effective reproduction numbers significantly fluctuated before June or after July but varied mildly in (0.5,2) in around June to July for three serotypes. The weekly effective reproduction number for HFMD was estimated based on weekly new cases from year 2010 to 2018, and in most years it peaked in the range of (1.6,2.0) in February to March as well as in the range of (1.0,1.2) in September to October. The wavelet analysis based on the time series of HFMD cases from 2008 to 2018 showed obvious annual and semi-annual cyclicity, while the inter-annual cycles are infeasible. In this study we found that CV-A6 shows the greatest transmission ability among these three pathogens while EV-A71 exhibits the weakest ability of transmission, and moreover, the estimated values of basic reproduction number in northwest China are lower than those in Singapore, Hongkong and Guangdong, which may be due to different climatic circumstances.

Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions

The future implications of climate change on malaria transmission at the global level have already been reported, however such evidences are scarce and limited in India. Here our study aims to assess, identify and map the potential effects of climate change on Plasmodium vivax (Pv) and Plasmodium falciparum (Pf) malaria transmission in India. A Fuzzy-based Climate Suitability Malaria Transmission (FCSMT) model under the GIS environment was generated using Temperature and Relative Humidity data, extracted from CORDEX South Asia for Baseline (1976-2005) and RCP 4.5 scenario for future projection by the 2030s (2021-2040). National malaria data were used at the model analysis stage. Model outcomes suggest that climate change may significantly increase the spatial spread of Pv and Pf malaria with a numerical increase in the transmission window's (TW) months, and a shift in the months of transmission. Some areas of the western Himalayan states are likely to have new foci of Pv malaria transmission. Interior parts of some southern and eastern states are likely to become more suitable for Pf malaria transmission. Study has also identified the regions with a reduction in transmission months by the 2030s, leading to unstable malaria, and having the potential for malaria outbreaks.

Climate drivers of malaria at its southern fringe in the Americas

In this work we analyze potential environmental drivers of malaria cases in Northwestern Argentina. We inspect causal links between malaria and climatic variables by means of the convergent cross mapping technique, which provides a causality criterion from the theory of dynamic systems. Analysis is based on 12 years of weekly malaria P. vivax cases in Tartagal, Salta, Argentina-at the southern fringe of malaria incidence in the Americas-together with humidity and temperature time-series spanning the same period. Our results show that there are causal links between malaria cases and both maximum temperature, with a delay of five weeks, and minimum temperature, with delays of zero and twenty two weeks. Humidity is also a driver of malaria cases, with thirteen weeks delay between cause and effect. Furthermore we also determined the sign and strength of the effects. Temperature has always a positive non-linear effect on cases, with maximum temperature effects more pronounced above 25°C and minimum above 17°C, while effects of humidity are more intricate: maximum humidity above 85% has a negative effect, whereas minimum humidity has a positive effect on cases. These results might be signaling processes operating at short (below 5 weeks) and long (over 12 weeks) time delays, corresponding to effects related to parasite cycle and mosquito population dynamics respectively. The non-linearities found for the strength of the effect of temperature on malaria cases make warmer areas more prone to higher increases in the disease incidence. Moreover, our results indicate that an increase of extreme weather events could enhance the risks of malaria spreading and re-emergence beyond the current distribution. Both situations, warmer climate and increase of extreme events, will be remarkably increased by the end of the century in this hot spot of climate change.

Climate drivers of hospitalizations for mycoses in Brazil

Climate can modulate human health at large spatial scales, but the influence of global, regional, and local environments remains poorly understood, especially for neglected diseases, such as mycoses. In this work, we present the correlation between climatic variables and hospitalizations for mycoses in Brazilian state capitals, evaluating the period of 2008 to 2016 at different time scales. The results indicate that climate modulates the hospitalizations for mycoses differently at annual and monthly time scales, with minimum temperature as a key climatic variable during periods of high prevalence in the 10 Brazilian capitals with the highest hospitalizations for mycoses rates. The greatest number of hospitalizations coincided with La Niña events, while a reduction was observed during El Niño events, thereby demonstrating the influence of the Pacific Interdecadal Climate Oscillation on the prevalence of mycoses in Brazil. At a regional scale, the mycoses burden in Brazil appears to respond differently to local and global climatic drivers.

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead

Malaria forecasts from dynamical systems have never been attempted at the health district or local clinic catchment scale, and so their usefulness for public health preparedness and response at the local level is fundamentally unknown. A pilot preoperational forecasting system is introduced in which the European Centre for Medium Range Weather Forecasts ensemble prediction system and seasonal climate forecasts of temperature and rainfall are used to drive the uncalibrated dynamical malaria model VECTRI to predict anomalies in transmission intensity 4 months ahead. It is demonstrated that the system has statistically significant skill at a number of sentinel sites in Uganda with high-quality data. Skill is also found at approximately 50% of the Ugandan health districts despite inherent uncertainties of unconfirmed health reports. A cost-loss economic analysis at three example sentinel sites indicates that the forecast system can have a positive economic benefit across a broad range of intermediate cost-loss ratios and frequency of transmission anomalies. We argue that such an analysis is a necessary first step in the attempt to translate climate-driven malaria information to policy-relevant decisions.

Comparison of stochastic and deterministic frameworks in dengue modelling

We perform estimations of compartment models for dengue transmission in rural Cambodia with increasing complexity regarding both model structure and the account for stochasticity. On the one hand, we successively account for three embedded sources of stochasticity: observation noise, demographic variability and environmental hazard. On the other hand, complexity in the model structure is increased by introducing vector-borne transmission, explicit asymptomatic infections and interacting virus serotypes. Using two sources of case data from dengue epidemics in Kampong Cham (Cambodia), models are estimated in the bayesian framework, with Markov Chain Monte Carlo and Particle Markov Chain Monte Carlo. We highlight the advantages and drawbacks of the different formulations in a practical setting. Although in this case the deterministic models provide a good approximation of the mean trajectory for a low computational cost, the stochastic frameworks better reflect and account for parameter and simulation uncertainty.

Transmission dynamics of re-emerging rabies in domestic dogs of rural China

Despite ongoing efforts to control transmission, rabies prevention remains a challenge in many developing countries, especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance. By taking advantage of detailed genomic and epidemiological data for the re-emerging rabies outbreak in Yunnan Province, China, collected between 1999 and 2015, we reconstruct the demographic and dispersal history of domestic dog rabies virus (RABV) as well as the dynamics of dog-to-dog and dog-to-human transmission. Phylogeographic analyses reveal a lower diffusion coefficient than previously estimated for dog RABV dissemination in northern Africa. Furthermore, epidemiological analyses reveal transmission rates between dogs, as well as between dogs and humans, lower than estimates for Africa. Finally, we show that reconstructed epidemic history of RABV among dogs and the dynamics of rabid dogs are consistent with the recorded human rabies cases. This work illustrates the benefits of combining phylogeographic and epidemic modelling approaches for uncovering the spatiotemporal dynamics of zoonotic diseases, with both approaches providing estimates of key epidemiological parameters.

Practical unidentifiability of a simple vector-borne disease model: Implications for parameter estimation and intervention assessment

Mathematical modeling has an extensive history in vector-borne disease epidemiology, and is increasingly used for prediction, intervention design, and understanding mechanisms. Many studies rely on parameter estimation to link models and data, and to tailor predictions and counterfactuals to specific settings. However, few studies have formally evaluated whether vector-borne disease models can properly estimate the parameters of interest given the constraints of a particular dataset. Identifiability analysis allows us to examine whether model parameters can be estimated uniquely-a lack of consideration of such issues can result in misleading or incorrect parameter estimates and model predictions. Here, we evaluate both structural (theoretical) and practical identifiability of a commonly used compartmental model of mosquito-borne disease, using the 2010 dengue epidemic in Taiwan as a case study. We show that while the model is structurally identifiable, it is practically unidentifiable under a range of human and mosquito time series measurement scenarios. In particular, the transmission parameters form a practically identifiable combination and thus cannot be estimated separately, potentially leading to incorrect predictions of the effects of interventions. However, in spite of the unidentifiability of the individual parameters, the basic reproduction number was successfully estimated across the unidentifiable parameter ranges. These identifiability issues can be resolved by directly measuring several additional human and mosquito life-cycle parameters both experimentally and in the field. While we only consider the simplest case for the model, we show that a commonly used model of vector-borne disease is unidentifiable from human and mosquito incidence data, making it difficult or impossible to estimate parameters or assess intervention strategies. This work illustrates the importance of examining identifiability when linking models with data to make predictions and inferences, and particularly highlights the importance of combining laboratory, field, and case data if we are to successfully estimate epidemiological and ecological parameters using models.

Climate change vulnerability in a tropical region based on environmental and socio-economic factors

The understanding of the regional and local dimensions of vulnerability due to climate change is essential to develop appropriate and targeted adaptation efforts. We assessed the local dimensions of vulnerability in the tropical state of Kerala, India, using a purposely developed vulnerability index, which accounts for both environmental and socio-economic factors. The large extents of coastal wetlands and lagoons and high concentration of mangrove forests make the state environmentally vulnerable. Low human development index, large population of socially deprived groups, which are dependent on the primary sector, and high population density make the state vulnerable from a socio-economic point of view. The present study investigates climate change vulnerability at the district level in the State of Kerala relying on a purposely developed composite vulnerability index that encompasses both socio-economic and environmental factors. The Kerala coast contains the socio-economically and ecologically most vulnerable regions, as demonstrated by a composite vulnerability index.

Climate Change and Women's Health: Impacts and Opportunities in India

Climate change impacts on health, including increased exposures to heat, poor air quality, extreme weather events, and altered vector-borne disease transmission, reduced water quality, and decreased food security, affect men and women differently due to biologic, socioeconomic, and cultural factors. In India, where rapid environmental changes are taking place, climate change threatens to widen existing gender-based health disparities. Integration of a gendered perspective into existing climate, development, and disaster-risk reduction policy frameworks can decrease negative health outcomes. Modifying climate risks requires multisector coordination, improvement in data acquisition, monitoring of gender specific targets, and equitable stakeholder engagement. Empowering women as agents of social change can improve mitigation and adaptation policy interventions.

Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors

In this study, experiments are conducted to gauge the relative importance of model, initial condition, and driving climate uncertainty for simulations of malaria transmission at a highland plantation in Kericho, Kenya. A genetic algorithm calibrates each of these three factors within their assessed prior uncertainty in turn to see which allows the best fit to a timeseries of confirmed cases. It is shown that for high altitude locations close to the threshold for transmission, the spatial representativeness uncertainty for climate, in particular temperature, dominates the uncertainty due to model parameter settings. Initial condition uncertainty plays little role after the first two years, and is thus important in the early warning system context, but negligible for decadal and climate change investigations. Thus, while reducing uncertainty in the model parameters would improve the quality of the simulations, the uncertainty in the temperature driving data is critical. It is emphasized that this result is a function of the mean climate of the location itself, and it is shown that model uncertainty would be relatively more important at warmer, lower altitude locations.

Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models

The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that time-varying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain.

The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh

Bangladesh is one of the world's most vulnerable countries for climate change. This observational study examined the association of temperature, humidity and rainfall with six common climate-sensitive infectious diseases in adults (malaria, diarrheal disease, enteric fever, encephalitis, pneumonia and bacterial meningitis) in northeastern Bangladesh. Subjects admitted to the adult medicine ward of a tertiary referral hospital in Sylhet, Bangladesh from 2008 to 2012 with a diagnosis of one of the six chosen climate-sensitive infectious diseases were enrolled in the study. Climate-related data were collected from the Bangladesh Meteorological Institute. Disease incidence was then analyzed against mean temperature, humidity and average rainfall for the Sylhet region. Statistical significance was determined using Mann-Whitney test, Chi-square test and ANOVA testing. 5033 patients were enrolled (58% male, 42% female, ratio 1.3:1). All six diseases showed highly significant (p = 0.01) rises in incidence between the study years 2008 (540 cases) and 2012 (1330 cases), compared with no significant rise in overall all-cause hospital admissions in the same period (p = 0.19). The highest number of malaria (135), diarrhea (266) and pneumonia (371) cases occurred during the rainy season. On the other hand, the maximum number of enteric fever (408), encephalitis (183) and meningitis (151) cases occurred during autumn, which follows the rainy season. A positive (P = 0.01) correlation was observed between increased temperature and the incidence of malaria, enteric fever and diarrhea, and a negative correlation with encephalitis, meningitis and pneumonia. Higher humidity correlated (P = 0.01) with a higher number of cases of malaria and diarrhea, but inversely correlated with meningitis and encephalitis. Higher incidences of encephalitis and meningitis occurred while there was low rainfall. Incidences of diarrhea, malaria and enteric fever, increased with rainfall, and then gradually decreased. The findings support a relationship between weather patterns and disease incidence, and provide essential baseline data for future large prospective studies.

Urbanization prolongs hantavirus epidemics in cities

Urbanization reduces exposure risk to many wildlife parasites and in general, improves overall health. However, our study importantly shows the complicated relationship between the diffusion of zoonotic pathogens and urbanization. Here, we reveal an unexpected relationship between hemorrhagic fever with renal syndrome incidence caused by a severe rodent-borne zoonotic pathogen worldwide and the process of urbanization in developing China. Our findings show that the number of urban immigrants is highly correlated with human incidence over time and also explain how the endemic turning points are associated with economic growth during the urbanization process. Our study shows that urbanizing regions of the developing world should focus their attention on zoonotic diseases.

Urbanization and rural–urban migration are two factors driving global patterns of disease and mortality. There is significant concern about their potential impact on disease burden and the effectiveness of current control approaches. Few attempts have been made to increase our understanding of the relationship between urbanization and disease dynamics, although it is generally believed that urban living has contributed to reductions in communicable disease burden in industrialized countries. To investigate this relationship, we carried out spatiotemporal analyses using a 48-year-long dataset of hemorrhagic fever with renal syndrome incidence (HFRS; mainly caused by two serotypes of hantavirus in China: Hantaan virus and Seoul virus) and population movements in an important endemic area of south China during the period 1963–2010. Our findings indicate that epidemics coincide with urbanization, geographic expansion, and migrant movement over time. We found a biphasic inverted U-shaped relationship between HFRS incidence and urbanization, with various endemic turning points associated with economic growth rates in cities. Our results revealed the interrelatedness of urbanization, migration, and hantavirus epidemiology, potentially explaining why urbanizing cities with high economic growth exhibit extended epidemics. Our results also highlight contrasting effects of urbanization on zoonotic disease outbreaks during periods of economic development in China.

Devising a method towards development of early warning tool for detection of malaria outbreak

Background & objectives

Uncertainty often arises in differentiating seasonal variation from outbreaks of malaria. The present study was aimed to generalize the theoretical structure of sine curve for detecting an outbreak so that a tool for early warning of malaria may be developed.

Methods

A 'case/mean-ratio scale' system was devised for labelling the outbreak in respect of two diverse districts of Assam and Rajasthan. A curve-based method of analysis was developed for determining outbreak and using the properties of sine curve. It could be used as an early warning tool for Plasmodium falciparum malaria outbreaks.

Result

In the present method of analysis, the critical C_max(peak value of sine curve) value of seasonally adjusted curve for P. falciparum malaria outbreak was 2.3 for Karbi Anglong and 2.2 for Jaisalmer districts. On case/mean-ratio scale, the C_max value of malaria curve between C_maxand 3.5, the outbreak could be labelled as minor while >3.5 may be labelled as major. In epidemic years, with mean of case/mean ratio of ≥1.00 and root mean square (RMS) ≥1.504 of case/mean ratio, outbreaks can be predicted 1-2 months in advance.

Interpretation & conclusions

The present study showed that in P. falciparum cases in Karbi Anglong (Assam) and Jaisalmer (Rajasthan) districts, the rise in C_maxvalue of curve was always followed by rise in average/RMS or both and hence could be used as an early warning tool. The present method provides better detection of outbreaks than the conventional method of mean plus two standard deviation (mean+2 SD). The identified tools are simple and may be adopted for preparedness of malaria outbreaks.

Modeling and inference for infectious disease dynamics: a likelihood-based approach

Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed-form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood maximization, promoting the study of novel data-motivated mechanistic models over the last decade. Reviewing these models is the focus of this paper. In particular, we highlight statistical aspects of these models like overdispersion, which is key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration.

Effects of complexity and seasonality on backward bifurcation in vector-host models

We study implications of complexity and seasonality in vector-host epidemiological models exhibiting backward bifurcation. Vector-host diseases represent complex infection systems that can vary in the transmission processes and population stages involved in disease progression. Seasonal fluctuations in external forcing factors can also interact in a complex way with internal host factors to govern the transmission dynamics. In backward bifurcation, the insufficiency of R0 < 1 for predicting the stability of the disease-free equilibrium (DFE) state arises due to existence of bistability (coexisting DFE and endemic equilibria) for a range of R0 values below one. Here we report that this region of bistability decreases with increasing complexity of vector-borne disease transmission as well as with increasing seasonality strength. The decreases in the bistability region are accompanied by a reduced force of infection acting on primary hosts. As a consequence, we show counterintuitively that a more complex vector-borne disease may be easier to control in settings of high seasonality.

Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead

Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.

A second-order iterated smoothing algorithm

Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact that direct computation of the likelihood is not possible in many practical situations. Iterated filtering methodologies enable maximization of the likelihood function using simulation-based sequential Monte Carlo filters. Doucet et al. (2013) developed an approximation for the first and second derivatives of the log likelihood via simulation-based sequential Monte Carlo smoothing and proved that the approximation has some attractive theoretical properties. We investigated an iterated smoothing algorithm carrying out likelihood maximization using these derivative approximations. Further, we developed a new iterated smoothing algorithm, using a modification of these derivative estimates, for which we establish both theoretical results and effective practical performance. On benchmark computational challenges, this method beat the first-order iterated filtering algorithm. The method's performance was comparable to a recently developed iterated filtering algorithm based on an iterated Bayes map. Our iterated smoothing algorithm and its theoretical justification provide new directions for future developments in simulation-based inference for latent variable models such as partially observed Markov process models.

Effects of reactive social distancing on the 1918 influenza pandemic

The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson's correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.

Interannual cycles of Hantaan virus outbreaks at the human-animal interface in Central China are controlled by temperature and rainfall

Hantavirus, a rodent-borne zoonotic pathogen, has a global distribution with 200,000 human infections diagnosed annually. In recent decades, repeated outbreaks of hantavirus infections have been reported in Eurasia and America. These outbreaks have led to public concern and an interest in understanding the underlying biological mechanisms. Here, we propose a climate-animal-Hantaan virus (HTNV) infection model to address this issue, using a unique dataset spanning a 54-y period (1960-2013). This dataset comes from Central China, a focal point for natural HTNV infection, and includes both field surveillance and an epidemiological record. We reveal that the 8-y cycle of HTNV outbreaks is driven by the confluence of the cyclic dynamics of striped field mouse (Apodemus agrarius) populations and climate variability, at both seasonal and interannual cycles. Two climatic variables play key roles in the ecology of the HTNV system: temperature and rainfall. These variables account for the dynamics in the host reservoir system and markedly affect both the rate of transmission and the potential risk of outbreaks. Our results suggest that outbreaks of HTNV infection occur only when climatic conditions are favorable for both rodent population growth and virus transmission. These findings improve our understanding of how climate drives the periodic reemergence of zoonotic disease outbreaks over long timescales.

Estimation of reproduction number and non stationary spectral analysis of dengue epidemic

In this work we analyze the post monsoon Dengue outbreaks by analyzing the transient and long term dynamics of Dengue incidences and its environmental correlates in Ahmedabad city in western India from 2005 to 2012. We calculate the reproduction number R_p using the growth rate of post monsoon Dengue outbreaks and biological parameters like host and vector incubation periods and vector mortality rate, and its uncertainties are estimated through Monte-Carlo simulations by sampling parameters from their respective probability distributions. Reduction in Female Aedes mosquito density required for an effective prevention of Dengue outbreaks is also calculated. The non stationary pattern of Dengue incidences and its climatic correlates like rainfall temperature is analyzed through Wavelet based methods. We find that the mean time lag between peak of monsoon and Dengue is 9 weeks. Monsoon and Dengue cases are phase locked from 2008 to 2012 in the 16 to maintain consistency make it "16 to 32" 32 weeks band. The duration of post monsoon outbreak has been increasing every year, especially post 2008, even though the intensity and duration of monsoon has been decreasing. Temperature and Dengue incidences show correlations in the same band, but phase lock is not stationary.

Limits to Causal Inference with State-Space Reconstruction for Infectious Disease

Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models to test hypotheses. Methods based on state-space reconstruction have been proposed to infer causal interactions in noisy, nonlinear dynamical systems. These “model-free” methods are collectively known as convergent cross-mapping (CCM). Although CCM has theoretical support, natural systems routinely violate its assumptions. To identify the practical limits of causal inference under CCM, we simulated the dynamics of two pathogen strains with varying interaction strengths. The original method of CCM is extremely sensitive to periodic fluctuations, inferring interactions between independent strains that oscillate with similar frequencies. This sensitivity vanishes with alternative criteria for inferring causality. However, CCM remains sensitive to high levels of process noise and changes to the deterministic attractor. This sensitivity is problematic because it remains challenging to gauge noise and dynamical changes in natural systems, including the quality of reconstructed attractors that underlie cross-mapping. We illustrate these challenges by analyzing time series of reportable childhood infections in New York City and Chicago during the pre-vaccine era. We comment on the statistical and conceptual challenges that currently limit the use of state-space reconstruction in causal inference.

Structure in the variability of the basic reproductive number (R0) for Zika epidemics in the Pacific islands

Before the outbreak that reached the Americas in 2015, Zika virus (ZIKV) circulated in Asia and the Pacific: these past epidemics can be highly informative on the key parameters driving virus transmission, such as the basic reproduction number (R0). We compare two compartmental models with different mosquito representations, using surveillance and seroprevalence data for several ZIKV outbreaks in Pacific islands (Yap, Micronesia 2007, Tahiti and Moorea, French Polynesia 2013-2014, New Caledonia 2014). Models are estimated in a stochastic framework with recent Bayesian techniques. R0 for the Pacific ZIKV epidemics is estimated between 1.5 and 4.1, the smallest islands displaying higher and more variable values. This relatively low range of R0 suggests that intervention strategies developed for other flaviviruses should enable as, if not more effective control of ZIKV. Our study also highlights the importance of seroprevalence data for precise quantitative analysis of pathogen propagation, to design prevention and control strategies.

DOI:http://dx.doi.org/10.7554/eLife.19874.001

Zika virus is an infectious disease primarily transmitted between people by mosquitoes. While most people develop mild flu-like symptoms, infection during pregnancy can interfere with how the baby’s head and brain develop. Until recently, the virus had only been seen sporadically in Africa and Asia, but since 2007, outbreaks have been recorded on several Pacific islands. In 2015, the Zika virus reached the Americas, and within six months over 1.5 million cases had been reported in Brazil alone.

There is an urgent need to understand how the Zika virus moves within a population in order to help policymakers, and public health professionals, plan treatment and control of outbreaks of the disease. Researchers often use predictive models to estimate how a disease will spread. A parameter commonly calculated by these models is the “basic reproductive number”, or R₀, which represents the average number of additional cases of the disease caused by one infected individual.

Using models that incorporated data from Zika virus outbreaks that occurred on several Pacific islands, Champagne et al. have produced estimates of R₀ that range from 1.5-4.1.

The R₀ values are greater than one, indicating that infection will spread within a population, but in the same range as those obtained for dengue fever, another closely related mosquito-borne disease. This suggests that by taking appropriate measures, the spread of Zika and dengue can be controlled to similar extents.

A closer look at the relationship between the population size and the predicted R₀ value for each Pacific island revealed an unexpected inverse relationship: the smaller the population, the larger the value of R₀. Since other regional factors may also explain these large differences between settings, further work is needed to disentangle context-specific from disease-specific factors. In this respect, data about seroprevalence (the number of people whose blood shows evidence of a past infection) in different populations is crucial for precisely analyzing the spread of Zika virus.

DOI:http://dx.doi.org/10.7554/eLife.19874.002

Effects of quasiperiodic forcing in epidemic models

We study changes in the bifurcations of seasonally driven compartmental epidemic models, where the transmission rate is modulated temporally. In the presence of periodic modulation of the transmission rate, the dynamics varies from periodic to chaotic. The route to chaos is typically through period doubling bifurcation. There are coexisting attractors for some sets of parameters. However in the presence of quasiperiodic modulation, tori are created in place of periodic orbits and chaos appears via finite torus doublings. Strange nonchaotic attractors (SNAs) are created at the boundary of chaotic and torus dynamics. Multistability is found to be reduced as a function of quasiperiodic modulation strength. It is argued that occurrence of SNAs gives an opportunity of asymptotic predictability of epidemic growth even when the underlying dynamics is strange.

Differential and enhanced response to climate forcing in diarrheal disease due to rotavirus across a megacity of the developing world

The role of climate forcing in the population dynamics of infectious diseases has typically been revealed via retrospective analyses of incidence records aggregated across space and, in particular, over whole cities. Here, we focus on the transmission dynamics of rotavirus, the main diarrheal disease in infants and young children, within the megacity of Dhaka, Bangladesh. We identify two zones, the densely urbanized core and the more rural periphery, that respond differentially to flooding. Moreover, disease seasonality differs substantially between these regions, spanning variation comparable to the variation from tropical to temperate regions. By combining process-based models with an extensive disease surveillance record, we show that the response to climate forcing is mainly seasonal in the core, where a more endemic transmission resulting from an asymptomatic reservoir facilitates the response to the monsoons. The force of infection in this monsoon peak can be an order of magnitude larger than the force of infection in the more epidemic periphery, which exhibits little or no postmonsoon outbreak in a pattern typical of nearby rural areas. A typically smaller peak during the monsoon season nevertheless shows sensitivity to interannual variability in flooding. High human density in the core is one explanation for enhanced transmission during troughs and an associated seasonal monsoon response in this diarrheal disease, which unlike cholera, has not been widely viewed as climate-sensitive. Spatial demographic, socioeconomic, and environmental heterogeneity can create reservoirs of infection and enhance the sensitivity of disease systems to climate forcing, especially in the populated cities of the developing world.