Characterising, modelling and mapping malaria occurrence and its mortality trend for precision public health

Research output: ThesisDoctoral Thesis


This work considers characterizing, modelling and mapping malaria occurrence and its mortality trend for Precision Public Health in Chimoio. Mozambique. Malaria is an ancient disease and a major public concern especially in the African continent. The majority of deaths occur among children living in Africa (91 %), where a child dies every minute and half from malaria. The data for malaria cases and mortality were obtained from the weekly BES from 2006 to 2014 and Civil Registration books from 2007 to 2014respectively. To model malaria cases ARIMA was used while for mortality trends, Intervention time series analysis (ITSA) was used. Package tscount and R version 3.3.2, Biestat 5.0 and SPSS were employed to fit, assess and predict model and statistical analysis. In Chimoio, malaria occurrence and mortality is increasing annually and presents a spatial and temporal pattern peaking during weeks 1 to 12 (January to March). The rural areas of the municipality have more malaria and mortality cases, followed by suburbs, and urban areas have fewer cases. Children under 5 years of age are three times more prone to get malaria than the rest of the population. The Chimoio climate seems ideal for malaria occurrence. Children between 1 – 4 years old are 13% of Chimoio population, but represent 25% of malaria mortalities. The entire municipality presents a malaria risk, 96% with moderate risk and 4% with high-risk areas. The use of Intervention time series analysis approach for modelling malaria mortality is suggested, and on owing to its flexibility and interpretation. The practicality of the statistical modelling method was validated to detect the lagged relationship between malaria cases and mortality. Based on the results, malaria cases and mortality can be predicted two months in advance. This modelling approach is robust, and can predict the expected number of malaria and mortality cases in advance. Thus, timely prevention and control measures can be effectively planned in Chimoio, such as the elimination of vector breeding places, correct time and place to spray insecticides, and awareness campaigns weeks before the malaria peak season. This can lead to a reduction in malaria cases, by knowing the best moment for spraying, saving time and cost of insecticide application and preventive programmes, and guiding smart environmental care (Precision Public Health).
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • NOVA Information Management School (NOVA IMS)
  • Mendes, Jorge Morais, Supervisor
  • Painho, Marco, Supervisor
Award date25 May 2018
Publication statusPublished - 25 May 2018


  • Malaria
  • Malaria mortality
  • Modelling
  • Forecasting
  • ITSA


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