ArticleMalaria journal2016
Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.
Article in Malaria journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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50 citing papers in PubMed.
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- Association of Climate Variables with Plasmodium vivax and Plasmodium falciparum Malaria Cases in Mandoto, Madagascar: A Statistical Modeling Study.The American journal of tropical medicine and hygiene · 2026Article
- Malaria time series analysis, forecasting, and development of curated intervention strategies for the elimination of malaria from Afghanistan.Malaria journal · 2026Article
- Cardiovascular hospitalization dynamics in Iran's second-largest city: A spatial and temporal perspective.PloS one · 2026Article
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- Deep Mixture of Linear Mixed Models for Complex Longitudinal Data.Statistics in medicine · 2025Article
- Comparison of ARIMA and FB-Prophet time series models for the prediction of national and regional malaria incidence in Uganda.Malaria journal · 2025Article
- Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.Scientific reports · 2025Article
- Forecasting malaria cases using climate variability in Sierra Leone.Malaria journal · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Time series forecasting of red blood cell demand in hematology patients using SARIMA and exponential smoothing models: a retrospective analysis in a Chinese tertiary hospital.Frontiers in medicine · 2025Article
- A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China.PloS one · 2025Article
- Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis.BMC infectious diseases · 2024Article
- Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.BMC public health · 2023Article
- Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020.JMIR public health and surveillance · 2022Observational
- Health Status of Afghan Refugees in Europe: Policy and Practice Implications for an Optimised Healthcare.International journal of environmental research and public health · 2022Review
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4 authors.
Funding
Abstract
backgroundMalaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region.
methodsThis study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models.
resultsTwo models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts.
conclusionResults indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.
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