Models for short term malaria prediction in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.</p> <p>Methods</p> <p>Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.</p> <p>Results</p> <p>The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.</p> <p>Conclusion</p> <p>Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.</p

A simple method for defining malaria seasonality

BACKGROUND: There is currently no standard way of defining malaria seasonality, resulting in a wide range of definitions reported in the literature. Malaria cases show seasonal peaks in most endemic settings, and the choice and timing for optimal malaria control may vary by seasonality. A simple approach is presented to describe the seasonality of malaria, to aid localized policymaking and targeting of interventions. METHODS: A series of systematic literature reviews were undertaken to identify studies reporting on monthly data for full calendar years on clinical malaria, hospital admission with malaria and entomological inoculation rates (EIR). Sites were defined as having 'marked seasonality' if 75% or more of all episodes occurred in six or less months of the year. A 'concentrated period of malaria' was defined as the six consecutive months with the highest cumulative proportion of cases. A sensitivity analysis was performed based on a variety of cut-offs. RESULTS: Monthly data for full calendar years on clinical malaria, all hospital admissions with malaria, and entomological inoculation rates were available for 13, 18, and 11 sites respectively. Most sites showed year-round transmission with seasonal peaks for both clinical malaria and hospital admissions with malaria, with a few sites fitting the definition of 'marked seasonality'. For these sites, consistent results were observed when more than one outcome or more than one calendar year was available from the same site. The use of monthly EIR data was found to be of limited value when looking at seasonal variations of malaria transmission, particularly at low and medium intensity levels. CONCLUSION: The proposed definition discriminated well between studies with 'marked seasonality' and those with less seasonality. However, a poor fit was observed in sites with two seasonal peaks. Further work is needed to explore the applicability of this definition on a wide-scale, using routine health information system data where possible, to aid appropriate targeting of interventions

Microclimate variables of the ambient environment deliver the actual estimates of the extrinsic incubation period of Plasmodium vivax and Plasmodium falciparum : A study from a malaria-endemic urban setting, Chennai in India

Background: Environmental factors such as temperature, relative humidity and their daily variation influence a range of mosquito life history traits and hence, malaria transmission. The standard way of characterizing environmental factors with meteorological station data need not be the actual microclimates experienced by mosquitoes within local transmission settings. Methods: A year-long study was conducted in Chennai, India to characterize local temperature and relative humidity (RH). Data loggers (Hobos) were placed in a range of probable indoor and outdoor resting sites of Anopheles stephensi. Recordings were taken hourly to estimate mean temperature and RH, together with daily temperature range (DTR) and daily relative humidity range. The temperature data were used to explore the predicted variation in extrinsic incubation period (EIP) of Plasmodium falciparum and Plasmodium vivax between microhabitats and across the year. Results: Mean daily temperatures within the indoor settings were significantly warmer than those recorded outdoors. DTR in indoor environments was observed to be modest and ranged from 2 to 6 °C. Differences in EIP between microhabitats were most notable during the hottest summer months of April-June, with parasite development predicted to be impaired for tiled houses and overhead tanks. Overall, the prevailing warm and stable conditions suggest rapid parasite development rate regardless of where mosquitoes might rest. Taking account of seasonal and local environmental variation, the predicted EIP of P. falciparum varied from a minimum of 9.1 days to a maximum of 15.3 days, while the EIP of P. vivax varied from 8.0 to 24.3 days. Conclusions: This study provides a detailed picture of the actual microclimates experienced by mosquitoes in an urban slum malaria setting. The data indicate differences between microhabitats that could impact mosquito and parasite life history traits. The predicted effects for EIP are often relatively subtle, but variation between minimum and maximum EIPs can play a role in disease transmission, depending on the time of year and where mosquitoes rest. Appropriate characterization of the local microclimate conditions would be the key to fully understand the effects of environment on local transmission ecology