Modelling the impact of climate variability and change on human health and diseases
Ngarakana-Gwasira, Ethel Tsungai
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Malaria is a life-threatening disease caused by parasites that are transmitted to humans through the bites of infected female mosquitoes. Almost half of the world’s population is at risk of malaria. Schistosomiasis is considered second only to malaria as the most devastating parasitic disease, estimated to affect 237 million people worldwide. The development, mortality and reproduction rates of the malaria and schistosomiasis parasites and their hosts are very sensitive to temperature and the availability of water bodies. The distribution and prevalence of the diseases are most likely to be affected by climate change. The aim of this thesis was to advance understanding of the potential effects of climate change on malaria and schistosomiasis transmission, using non linear differential equations. In addition, the study also sought to assess the role of mathematical models in evaluating the impact of climate variability and change on malaria and schistosomiasis transmission. The work in this thesis focused on investigating the effects of climate on malaria and schistosomiasis transmission in Africa and South America. Climate driven deterministic models were developed separately for malaria, schistosomiasis and malaria-schistosomiasis coinfection. Mathematical models of human population dynamics and the vector population dynamics were developed for both malaria and schistosomiasis. For both diseases, temperature-dependent stages of the parasites in their life cycles are considered. Temperature and rainfall were incorporated in the models to explore the effects of climate variability and change on the diseases transmission dynamics. The equilibrium states for the models were determined and analysed. The reproductive rates were computed for each model and accordingly analysed. Mathematical packages (Mathematica, Matlab and C++) were used to perform sensitivity analyses and numerical simulations. Projections for future transmission dynamics were made from climate change projection models in order to inform policy makers on how to deal with the diseases in the future. Results from the malaria model suggest that temperature range 23oC to 38oC is ideal for malaria transmission. The reproduction number increases as temperature increases to attain a maximum at 31.5oC, beyond which the reproduction number starts declining. This result suggests the optimal temperature for malaria transmission is around 31oC. The analytic results are also supported by numerical simulations which show an increase in malaria cases as temperature increases to about 38oC and a decrease thereafter. Furthermore, results from model analysis suggest daily rainfall in the range of 15 − 17mm is ideal for the spread of malaria. The models’ reproductive rates were simulated using climate models for Africa to determine the current transmission patterns and to aid prediction of future trends. The results of the simulated current transmission pattern of malaria fall within the observed spatial distribution of falciparum limits on the African continent. Results from future projections of malaria transmission suggest that due to climate change, endemic malaria will die out on the southern fringe of the disease map in Africa by 2040, while malaria endemicity is going to become a problem in the African highlands. A drying trend is the likely driving force for the reduction in malaria transmission in the regions to the south of the continent, while a warming trend is the likely factor driving the projected increase in malaria endemicity in the highlands, although increases in malaria incidences in these areas can also be attributed to socioeconomic factors such as land use change and drug resistance. The model has the following limitations: it did not consider the role of human migration, other climate variables, in particular relative humidity as the tropical anopheline mosquitoes prefer humidities above 60% and the role of socioeconomic factors inmalaria transmission dynamics. Despite these limitations, the model is reasonable enough to be able to give a realistic picture of malaria in the African continent. Thus, results from the study will be useful at various levels of decision making, for example, in setting up an early warning system and sustainable strategies for climate change adaptation for malaria vectors control programmes in Africa. These results can be generalized to other tropical regions outside Africa. A mathematical model to explore the impact of temperature and rainfall (in the context of its effect on water bodies) on schistosomiasis transmission is presented as a system of differential equations and analysed. The model analysis suggests that the optimal temperature for schistosomiasis transmission is around 23OC. Geographical information systems (GIS) was used to map the reproduction number for Zimbabwe using temperature and rainfall data from 1950 to 2000. It was noted that high reproduction numbers, which suggest high incidences of schistosomiasis, are found in the Zambezi valley catchment area and the lowveld of the country. Amathematical model for schistosomiasis andmalaria coinfection incorporating rainfall and temperature was developed and analysed. The coinfection reproduction number was computed and mapped on the continents of Africa and South America. Results from the mapping suggest that environmental ambient conditions in the equatorial regions of Africa and Latin America promote malaria and schistosomiasis coinfection with a heavier burden of coinfection in South America, especially in Brazil. Within Africa, there are some countries where it is beneficial to target both diseases, for example Angola, Democratic Republic of Congo and Madagascar. However there are some areas where targeting Malaria only is warranted. In the sub-tropical regions, including Namibia, South Africa, the greater part of Zimbabwe and the areas on the northern fringe of the Sahara, schistosomiasis is more dominant than malaria. Results also show that coinfection is a greater problem in general in South America than in Africa. These findings suggest that both schistosomiasis and malaria control programmes should be intensified in these regions of Africa and South America. The results of this study can be used to identify areas which need special attention with regard to malaria and schistosomiasis control. This can be extended to incorporate other aspects like the terrain of the region under study to capture the real transmission dynamics of schistosomiasis and malaria.
Additional Citation InformationNgarakana-Gwasira, E. T. (2016). Modelling the impact of climate variability and change on human health and diseases (Unpublished doctoral thesis). University of Zimbabwe, Harare.
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