Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning.

This study was conducted to quantify the association between meteorological variables and incidence of Plasmodium falciparum in areas with unstable malaria transmission in Ethiopia. We used morbidity data pertaining to microscopically confirmed cases reported from 35 sites throughout Ethiopia over a period of approximately 6-7 years. A model was developed reflecting biological relationships between meteorological and morbidity variables. A model that included rainfall 2 and 3 months earlier, mean minimum temperature of the previous month and P. falciparum case incidence during the previous month was fitted to morbidity data from the various areas. The model produced similar percentages of over-estimation (19.7% of predictions exceeded twice the observed values) and under-estimation (18.6%, were less than half the observed values). Inclusion of maximum temperature did not improve the model. The model performed better in areas with relatively high or low incidence (>85% of the total variance explained) than those with moderate incidence (55-85% of the total variance explained). The study indicated that a dynamic immunity mechanism is needed in a prediction model. The potential usefulness and drawbacks of the modelling approach in studying the weather-malaria relationship are discussed, including a need for mechanisms that can adequately handle temporal variations in immunity to malaria

Entomological surveillance following a long-lasting insecticidal net universal coverage campaign in Midwestern Uganda.

BACKGROUND: A universal coverage campaign (UCC) with long-lasting insecticidal nets (LLINs) was implemented in four districts in Midwestern Uganda in 2009-2010. Entomological surveys were carried out to monitor changes in vector density, behaviour and malaria transmission following this intervention. METHODS: Anopheles mosquitoes were collected using CDC light traps quarterly and human landing catch twice a year in four sites. Collections were done at baseline before the campaign and over a three-year period following the campaign. Plasmodium falciparum circumsporozoite enzyme-linked immunosorbent assays were performed. A subset of anophelines were molecularly identified to species, and kdr L1014S frequencies were determined. RESULTS: The prevailing malaria vector in three sites was Anopheles gambiae s.l. (>97 %), with An. funestus s.l. being present in low numbers only. An. gambiae s.s. dominated (> 95 %) over An. arabiensis within A. gambiae s.l. In the remaining site, all three vector species were observed, although their relative densities varied among seasons and years. Vector densities were low in the year following the UCC but increased over time. Vector infectivity was 3.2 % at baseline and 1.8 % three years post-distribution (p = 0.001). The daily entomological inoculation rate (EIR) in 2012 varied between 0.0-0.98 for the different sites compared to a baseline EIR that was between 0.0-5.8 in 2009. There was no indication of a change in indoor feeding times, and both An. gambiae s.l. and An. funestus s.l. continued to feed primarily after midnight with vectors being active until the early morning. Kdr L1014S frequencies were already high at baseline (53-85 %) but increased significantly in all sites over time. CONCLUSIONS: The entomological surveys indicate that there was a reduction in transmission intensity coinciding with an increase in use of LLINs and other antimalarial interventions in areas of high malaria transmission. There was no change in feeding behaviour, and human-vector contact occurred indoors and primarily after midnight constantly throughout the study. Although the study was not designed to evaluate the effectiveness of the intervention compared to areas with no such intervention, the reduction in transmission occurred in an area with previously stable malaria, which seems to indicate a substantial contribution of the increased LLIN coverage

Variations in entomological indices in relation to weather patterns and malaria incidence in East African highlands: implications for epidemic prevention and control

<p>Abstract</p> <p>Background</p> <p>Malaria epidemics remain a significant public health issue in the East African highlands. The aim of this study was to monitor temporal variations in vector densities in relation to changes in meteorological factors and malaria incidence at four highland sites in Kenya and Uganda and to evaluate the implications of these relationships for epidemic prediction and control.</p> <p>Methods</p> <p>Mosquitoes were collected weekly over a period of 47 months while meteorological variables and morbidity data were monitored concurrently. Mixed-effects Poisson regression was used to study the temporal associations of meteorological variables to vector densities and of the latter to incidence rates of <it>Plasmodium falciparum</it>.</p> <p>Results</p> <p><it>Anopheles gambiae </it>s.s. was the predominant vector followed by <it>Anopheles arabiensis</it>. <it>Anopheles funestus </it>was also found in low densities. Vector densities remained low even during periods of malaria outbreaks. Average temperature in previous month and rainfall in previous two months had a quadratic and linear relationship with <it>An. gambiae </it>s.s. density, respectively. A significant statistical interaction was also observed between average temperature and rainfall in the previous month. Increases in densities of this vector in previous two months showed a linear relationship with increased malaria incidence.</p> <p>Conclusion</p> <p>Although epidemics in highlands often appear to follow abnormal weather patterns, interactions between meteorological, entomological and morbidity variables are complex and need to be modelled mathematically to better elucidate the system. This study showed that routine entomological surveillance is not feasible for epidemic monitoring or prediction in areas with low endemicity. However, information on unusual increases in temperature and rainfall should be used to initiate rapid vector surveys to assess transmission risk.</p

Costs of early detection systems for epidemic malaria in highland areas of Kenya and Uganda

BACKGROUND: Malaria epidemics cause substantial morbidity and mortality in highland areas of Africa. The costs of detecting and controlling these epidemics have not been explored adequately in the past. This study presents the costs of establishing and running an early detection system (EDS) for epidemic malaria in four districts in the highlands of Kenya and Uganda. METHODS: An economic costing was carried out from the health service provider's perspective in both countries. Staff time for data entry and processing, as well as supervising and coordinating EDS activities at district and national levels was recorded and associated opportunity costs estimated. A threshold analysis was carried out to determine the number of DALYs or deaths that would need to be averted in order for the EDS to be considered cost-effective. RESULTS: The total costs of the EDS per district per year ranged between US$ 14,439 and 15,512. Salaries were identified as major cost-drivers, although their relative contribution to overall costs varied by country. Costs of relaying surveillance data between facilities and district offices (typically by hand) were also substantial. Data from Uganda indicated that 4% or more of overall costs could potentially be saved by switching to data transfer via mobile phones. Based on commonly used thresholds, 96 DALYs in Uganda and 103 DALYs in Kenya would need to be averted annually in each district for the EDS to be considered cost-effective. CONCLUSION: Results from this analysis suggest that EDS are likely to be cost-effective. Further studies that include the costs and effects of the health systems' reaction prompted by EDS will need to be undertaken in order to obtain comprehensive cost-effectiveness estimates

Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003

BACKGROUND: The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. METHODS: The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. RESULTS: The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range +/- 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. CONCLUSION: This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area

One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide

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

Determinants of the accuracy of rapid diagnostic tests in malaria case management: evidence from low and moderate transmission settings in the East African highlands

BACKGROUND: The accuracy of malaria diagnosis has received renewed interest in recent years due to changes in treatment policies in favour of relatively high-cost artemisinin-based combination therapies. The use of rapid diagnostic tests (RDTs) based on histidine-rich protein 2 (HRP2) synthesized by Plasmodium falciparum has been widely advocated to save costs and to minimize inappropriate treatment of non-malarial febrile illnesses. HRP2-based RDTs are highly sensitive and stable; however, their specificity is a cause for concern, particularly in areas of intense malaria transmission due to persistence of HRP2 antigens from previous infections. METHODS: In this study, 78,454 clinically diagnosed malaria patients were tested using HRP2-based RDTs over a period of approximately four years in four highland sites in Kenya and Uganda representing hypoendemic to mesoendemic settings. In addition, the utility of the tests was evaluated in comparison with expert microscopy for disease management in 2,241 subjects in two sites with different endemicity levels over four months. RESULTS: RDT positivity rates varied by season and year, indicating temporal changes in accuracy of clinical diagnosis. Compared to expert microscopy, the sensitivity, specificity, positive predictive value and negative predictive value of the RDTs in a hypoendemic site were 90.0%, 99.9%, 90.0% and 99.9%, respectively. Corresponding measures at a mesoendemic site were 91.0%, 65.0%, 71.6% and 88.1%. Although sensitivities at the two sites were broadly comparable, levels of specificity varied considerably between the sites as well as according to month of test, age of patient, and presence or absence of fever during consultation. Specificity was relatively high in older age groups and increased towards the end of the transmission season, indicating the role played by anti-HRP2 antibodies. Patients with high parasite densities were more likely to test positive with RDTs than those with low density infections. CONCLUSION: RDTs may be effective when used in low endemicity situations, but high false positive error rates may occur in areas with moderately high transmission. Reports on specificity of RDTs and cost-effectiveness analyses on their use should be interpreted with caution as there may be wide variations in these measurements depending upon endemicity, season and the age group of patients studied

Space-time variation of malaria incidence in Yunnan province, China

Abstract Background Understanding spatio-temporal variation in malaria incidence provides a basis for effective disease control planning and monitoring. Methods Monthly surveillance data between 1991 and 2006 for Plasmodium vivax and Plasmodium falciparum malaria across 128 counties were assembled for Yunnan, a province of China with one of the highest burdens of malaria. County-level Bayesian Poisson regression models of incidence were constructed, with effects for rainfall, maximum temperature and temporal trend. The model also allowed for spatial variation in county-level incidence and temporal trend, and dependence between incidence in June–September and the preceding January–February. Results Models revealed strong associations between malaria incidence and both rainfall and maximum temperature. There was a significant association between incidence in June–September and the preceding January–February. Raw standardised morbidity ratios showed a high incidence in some counties bordering Myanmar, Laos and Vietnam, and counties in the Red River valley. Clusters of counties in south-western and northern Yunnan were identified that had high incidence not explained by climate. The overall trend in incidence decreased, but there was significant variation between counties. Conclusion Dependence between incidence in summer and the preceding January–February suggests a role of intrinsic host-pathogen dynamics. Incidence during the summer peak might be predictable based on incidence in January–February, facilitating malaria control planning, scaled months in advance to the magnitude of the summer malaria burden. Heterogeneities in county-level temporal trends suggest that reductions in the burden of malaria have been unevenly distributed throughout the province

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Background: Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods: Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results: Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions: This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors