Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability.

BACKGROUND: Previous studies have demonstrated the feasibility of early-warning systems for epidemic malaria informed by climate variability. Whereas modelling approaches typically assume stationary conditions, epidemiological systems are characterized by changes in intervention measures over time, at scales typically longer than inter-epidemic periods. These trends in control efforts preclude simple application of early-warning systems validated by retrospective surveillance data; their effects are also difficult to distinguish from those of climate variability itself. METHODS: Rainfall-driven transmission models for falciparum and vivax malaria are fitted to long-term retrospective surveillance data from four districts in northwest India. Maximum-likelihood estimates (MLEs) of model parameters are obtained for each district via a recently introduced iterated filtering method for partially observed Markov processes. The resulting MLE model is then used to generate simulated yearly forecasts in two different ways, and these forecasts are compared with more recent (out-of-fit) data. In the first approach, initial conditions for generating the predictions are repeatedly updated on a yearly basis, based on the new epidemiological data and the inference method that naturally lends itself to this purpose, given its time-sequential application. In the second approach, the transmission parameters themselves are also updated by refitting the model over a moving window of time. RESULTS: Application of these two approaches to examine the predictability of epidemic malaria in the different districts reveals differences in the effectiveness of intervention for the two parasites, and illustrates how the 'failure' of predictions can be informative to evaluate and quantify the effect of control efforts in the context of climate variability. The first approach performs adequately, and sometimes even better than the second one, when the climate remains the major driver of malaria dynamics, as found for Plasmodium vivax for which an effective clinical intervention is lacking. The second approach offers more skillful forecasts when the dynamics shift over time, as is the case of Plasmodium falciparum in recent years with declining incidence under improved control. CONCLUSIONS: Predictive systems for infectious diseases such as malaria, based on process-based models and climate variables, can be informative and applicable under non-stationary conditions

Climate change and human health : Indian context

The article reviews the issue of climate change and health in the Indian context. The importance of climatechange leading to estimated loss of above 2.5 million DALYs in southeast Asia, mortality due to heat waves, andthe importance of air quality related respiratory diseases, disasters due to excessive floods, malnutrition due toreduction in rice, maize and sorghum crops etc. Latest work undertaken in India, vis-a-vis current scenario andneed for further work has been discussed. There is felt need of further studies on assessing the impact on dengueand chikungunya as the transmission dynamics of these diseases involve water availability, storage and lifestyle, etc. Uncertainties and knowledge gaps identified in the studies undertaken so far have also been highlighted.As regards to vector borne diseases, there is a need to concentrate in the areas which are presently free frommalaria and with use of best available tools of interventions in already disease endemic areas like northeasternstates, the risk of climate change impacts can be minimized

Climate forcing and desert malaria: the effect of irrigation

BACKGROUND: Rainfall variability and associated remote sensing indices for vegetation are central to the development of early warning systems for epidemic malaria in arid regions. The considerable change in land-use practices resulting from increasing irrigation in recent decades raises important questions on concomitant change in malaria dynamics and its coupling to climate forcing. Here, the consequences of irrigation level for malaria epidemics are addressed with extensive time series data for confirmed Plasmodium falciparum monthly cases, spanning over two decades for five districts in north-west India. The work specifically focuses on the response of malaria epidemics to rainfall forcing and how this response is affected by increasing irrigation. METHODS AND FINDINGS: Remote sensing data for the Normalized Difference Vegetation Index (NDVI) are used as an integrated measure of rainfall to examine correlation maps within the districts and at regional scales. The analyses specifically address whether irrigation has decreased the coupling between malaria incidence and climate variability, and whether this reflects (1) a breakdown of NDVI as a useful indicator of risk, (2) a weakening of rainfall forcing and a concomitant decrease in epidemic risk, or (3) an increase in the control of malaria transmission. The predictive power of NDVI is compared against that of rainfall, using simple linear models and wavelet analysis to study the association of NDVI and malaria variability in the time and in the frequency domain respectively. CONCLUSIONS: The results show that irrigation dampens the influence of climate forcing on the magnitude and frequency of malaria epidemics and, therefore, reduces their predictability. At low irrigation levels, this decoupling reflects a breakdown of local but not regional NDVI as an indicator of rainfall forcing. At higher levels of irrigation, the weakened role of climate variability may be compounded by increased levels of control; nevertheless this leads to no significant decrease in the actual risk of disease. This implies that irrigation can lead to more endemic conditions for malaria, creating the potential for unexpectedly large epidemics in response to excess rainfall if these climatic events coincide with a relaxation of control over time. The implications of our findings for control policies of epidemic malaria in arid regions are discussed

Air versus water temperature of aquatic habitats in Delhi: Implications for transmission dynamics of Aedes aegypti

The present study was planned to characterize the microclimate experienced by Aedes larvae in different breeding habitats by determining the temperature variations in water kept in containers during different months under natural conditions. The study was conducted in three municipal zones of Delhi. In each site, four types of container material (plastic, cement, iron and ceramic) were chosen for recording the water temperature in the containers. Daily air and water temperatures (mean, maximum and minimum values) recorded by HOBO and Tidbit data loggers, respectively, were compared using analysis of variance and Tukey’s honest significant difference (HSD) tests. Mean monthly temperature of water varied from 16.9 to 33.0 °C in tin containers, 17.3 to 35.6°C in plastic containers, 14.3 to 28.5°C in ceramic pots, 23.3 to 30.4°C in cemented underground tanks (UGT) and 15.8 to 35.1°C in cemented overhead tanks (OHTs). Corresponding values for the air temperature ranged from 17.7 to 36.1°C. The difference between temperature of water in the containers and air temperature was highest for ceramic pots. Daily mean, maximum and minimum temperatures recorded by different data loggers differed significantly (P<0.05). When Tukey HSD test was applied for data analysis, the daily mean air temperature differed significantly from the water temperature in tin and ceramic pots as well as cemented OHTs. The temperature of water in the different breeding habitats investigated was lower than the air temperature. Moreover, actual air temperature as recorded by HOBO was higher than the temperature recorded by local weather stations. Considering the ongoing climate change, cemented UGT and earthen pots may be more productive breeding habitats for the Aedes mosquito in the near future, while plastic and cemented OHTs might no longer be suitable for Aedes breeding

Of cattle, sand flies and men : a systematic review of risk factor analyses for South Asian visceral leishmaniasis and implications for elimination

Background: Studies performed over the past decade have identified fairly consistent epidemiological patterns of risk factors for visceral leishmaniasis (VL) in the Indian subcontinent. Methods and Principal Findings: To inform the current regional VL elimination effort and identify key gaps in knowledge, we performed a systematic review of the literature, with a special emphasis on data regarding the role of cattle because primary risk factor studies have yielded apparently contradictory results. Because humans form the sole infection reservoir, clustering of kala-azar cases is a prominent epidemiological feature, both at the household level and on a larger scale. Subclinical infection also tends to show clustering around kala-azar cases. Within villages, areas become saturated over a period of several years; kala-azar incidence then decreases while neighboring areas see increases. More recently, post kalaazar dermal leishmaniasis (PKDL) cases have followed kala-azar peaks. Mud walls, palpable dampness in houses, and peridomestic vegetation may increase infection risk through enhanced density and prolonged survival of the sand fly vector. Bed net use, sleeping on a cot and indoor residual spraying are generally associated with decreased risk. Poor micronutrient status increases the risk of progression to kala-azar. The presence of cattle is associated with increased risk in some studies and decreased risk in others, reflecting the complexity of the effect of bovines on sand fly abundance, aggregation, feeding behavior and leishmanial infection rates. Poverty is an overarching theme, interacting with individual risk factors on multiple levels. Conclusions: Carefully designed demonstration projects, taking into account the complex web of interconnected risk factors, are needed to provide direct proof of principle for elimination and to identify the most effective maintenance activities to prevent a rapid resurgence when interventions are scaled back. More effective, short-course treatment regimens for PKDL are urgently needed to enable the elimination initiative to succeed

Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing

Kyasanur forest disease and climatic attributes in India

Background & objectives: In India, Kyasanur Forest Disease has been reported from the states of Karnataka, Kerala, Goa, and Maharashtra. The relationship between climatic factors and transmission of KFD remains untouched, therefore, the present study was undertaken. Methods: Based on the occurrence of cases, Shivamogga district (Karnataka) and Wayanad district in Kerala and northern Goa (Goa state) were selected for the study. Data on the incidence of KFD and climate factors were collected from concerned authorities. To determine the relationship between dependent and independent variables, spearman’s correlation was calculated for monthly as well as with lag months. Results: KFD cases and temperature (°C) were found significantly correlated up to 1 months’ lag period (p1100-2400mm. Interpretation & conclusion: The analysis of three sites revealed that with the increase in temperature, the intensity of KFD transmission decreases as corroborated by the seasonal fluctuations in Shivamogga, Goa and Wayanad. High precipitation from June to October rovides suitable ecology to tick vector and sets in transmission season from November to May when cumulative precipitation is <500 mm