Introduction
The present and potential future effect of climatic change on malaria is one of the key interests as far as public health is concerned. “The spatial limits of Plasmodium falciparum coupled with its endemicity compared with different historic maps give strange insights into the dynamic global malaria epidemiology over the last century” (Maslin, 2004, p. 31). There will be a comparison of the intensity of the changes to the magnitude of the impacts on malaria endemicity proposed within the future scenarios of the climate. This is related with broadly utilized interventions of public health.
The findings were mainly two and to some extend ignored the impacts in relation to malaria and climate change. First and foremost, the broad claims, which state that, the increasing average temperatures have already caused an increase in the global morbidity of malaria and mortality. These two are widely at odds with the decreasing global trends observed in its geographic extent and endemicity. Secondly, the future impacts anticipated of the increasing temperatures on endemicity are to some small extent lesser than the changes seen two centuries ago (Molineaux, 1988).
Significance of the Problem
There is the likelihood of climate change affecting the transmission of various vector-borne diseases like malaria. In addition, the dynamics of malaria transmission is multi-factorial. It is normally enhanced by different agricultural activities, urbanization, socioeconomic conditions, migration, water availability and measures of intervention and projections founded on the basis of different climatic parameters. All these factors should have been observed with a lot of certainty. Instead, they have been utilized as preparedness guidelines within the susceptible areas to enhance health infrastructures, efficient education in health and the utilization of the perfect present intervention instruments to come with the climate change threat.
Purpose of the Study
The paper was defining a framework to be used in examining the impact of global warming on malaria. The framework is essential for the sector of public health to establish the risk of malaria within distinct geographic regions and demographics. The disease brings about the concept of third world countries against the developed world on the basis of the variations in the abilities to deal with epidemics originating from climatic change (World Health Organization, 2008).
Historical Background of Global Climate Change/Global Warming
Approximately 90 percent of malaria cases occur in Africa. Within the past ten years, the malaria incidence has been rising at a tremendous rate. Global warming is likely to result into many impacts on health plus alterations in the seasonal transmission and distribution of vector-borne diseases like malaria. On the contrary, the levels of these impacts continue to produce a lot of heated debate. This is the case especially in the proposed impact of global warming on the world malaria distribution. Within this distribution, various approaches have caused broadly varying estimates. An important issue encountered by the majority of researchers is the lack of comprehensive and high quality empirical data to be used in the validation of the utilized models.
The relationship between the distribution of malaria and climate has been established before. The transmissions sustained depend on favorable environmental conditions for both parasite and vector. The impact of temperature on the duration of the sporogenic cycle of the parasite for malaria and the survival of the vector are very essential. A couple of methods can be used to come up with an estimation of the changes in the global malaria distribution in cases of global climate change. (Russel, 1963, p.56)
One of the approaches depends on a biological model which gives a prediction of a wide increase in the potential of malaria. The utilization of the current distribution of malaria to obtain the model led to areas which were sustainable climatically appropriate for transmission although malaria had been controlled. This included the northern regions of Australia which skew the outcomes. Genetic limitations of continental or global statistically-driven models are the sets of data utilized to develop models statistically. A large parasite survey set is carried out in Africa to come up with a validated malaria model of transmission. This also provides a projection of the impact of three climatic cases.
Non-Human Safety Findings
The model illustrated specificity and sensitivity of 96 percent and 63 percent, respectively. This was within the first month of temporal accuracy. Additionally, this was in comparison to the surveys of the parasite. There was an estimation that averagely, there are approximately three billion individual-exposure months. The incidence of malaria seasonally is denoted by a wave of infection during spring. The seasonal malaria amplitude varies in all regions of the world. Seasonal malaria within the tropics takes place with substantial normality at similar times annually. In these areas, the development of malaria varies annually. This is inclusive with the rise in the transmission frequency. A good example is Malaysia which contains two periodic malaria waves. The seasonality of malaria in some places occurs as a result of climatic conditions, which pave the way, for the occasional or periodic vector and parasite development.
Toxicity of Global Climate Change/Global Warming
The mixture of global warming and environmental degradation has resulted into favorable conditions for the emergence, resurgence and transmission of malaria. Warmer and a bit of wet weather may be spreading already to regions where they were not found before. Hence, a warming climate coupled with broad ecological alterations may be enhancing widespread changes in the patterns of malaria.
Hence, the toxic effects of global warming are as follows. First and foremost, global warming has resulted into creating of conditions very favorable to outbreak of malaria and other infectious diseases. It has also led to the increase in the potential for malaria and other vector-borne disease transmission. Consequently, a lot of individuals have already been exposed to the disease. On the other hand, it has hindered the future control of the disease. In conclusion, the combination of environmental degradation and climatic change can come up with the necessary conditions for the outbreak, resurgence and the emergence of malaria and other vector-borne diseases.
Interactions
The cool areas in the highlands of most tropical countries have anciently been taken as malaria free. As a result of the transmission and spread of the disease into some of these places, it has been proposed that the extension of the geographic range of the disease is because of global warming. Despite the fact that warm conditions would enhance increased breeding and survival of mosquitoes which transmit malaria parasite, this could potentially act as a stimulant for the transmission of the disease. Additionally, there has been a lot of debate on whether the driven expansion of climatic change is really occurring. This controversy has become part and parcel of the broad debate on climate change. The debate is also on if the increasing global temperatures will cause an increase in the prevalence malaria rates and other infectious diseases. Various studies have indicated that the malaria transmission biology is complicated and involves interactions between many, constantly dynamic, intrinsic and extrinsic factors, most of which cannot be established easily. The research, which has been, carried out before concerning the relationship between temperature and malaria within the highlands of East Africa was based on some few questions (Russel, 1963).
The questions include if, in the real sense malaria, cases are re-emerging or increasing. The other question concerns the increasing temperatures. It talks about whether there exists a warming tend within the highlands and if it has any relationship whatsoever with global warming. The last question asks whether there exists a warming trend and the trend’s casual relationship with malaria incidence trends. The major question is if the increasing temperatures have played any role in the growth which has been experienced in these regions in the incidence of malaria’s clinical cases. These researchers used data which provided tangible evidence of resurgence and the emergence of malaria around some areas within the tropics. On the contrary, a lot of factors including the rising resistance to drugs which treat malaria. This could have resulted into the rise in the number of cases. After the research, a serious conclusion was reached. The conclusion was that the climate was not to be dismissed as a capable stimulant for the seen rise in malaria observed within the region during recent times. On the other hand, the relative study of the research was to be compared to other factors for further scrutiny.
The research illustrated the requirement for multi-sectorial cooperation within the malaria epidemiology study. Additionally, the research highlighted that services of climate are lately being developed which are appropriate to the attainment of targets for development like the Millennium Development Goals (MDGs). Also, it was appropriate to the analysis of vector-borne disease within the climate variability context. There was a continued analysis that that represented an essential new chance for the community of malaria. This should elaborate the research questions which would be likely to be addressed by the new services.
Methods of Measurement and Importance in Research
“There was the production of a spatiotemporally validated model of Plasmodium falciparum transmission of malaria in the African continent” (Fisher, 1996, p.43). This was carried out against approximately 3792 surveys of parasites. “We utilized variable scenarios of climate from the Hadley Centre global model of climate (HAD CM3) experiments of the climate” (Fisher, 1996, p.44). After this, there was the projection of the potential impact of climate change on the prevailing patterns of transmission.
Data
“Mean long-term monthly temperature and rainfall data was utilized as the foundation for the seasonality model” Thomson, & Connor, 2001, p.438). The gridded surfaces were on the basis of data from a weather station from 1920- 1990. It had a spatial resolution of approximately 0.05 degrees. The temperature data had a standard deviation of 0.5 degrees Celsius. The monthly average precipitation data had errors of 10-39 percent. The data of population utilized was an interpolated surface which contained a grid with a resolution of 0.042 degrees with the population estimates of 1994. In the overall, the population estimates’ uncertainty is likely to increase. On the contrary, it will remain within the normal error range related with the figures of the census for the developing world. A low greenhouse emission, B1 was utilized. Others included a medium-high emission, A2a and a high emission Intergovernmental Panel for Climatic Change (IPCC) scenarios of climate. This was generated alongside the HadCM3 model. The scenarios of climate were distinct in the concomitant rise, in global average temperature. This was under HadCM3 and was because of possible future economic, political, technical and social developments influencing emissions of greenhouse gases.
An example was between 1990 and 2100, A2a, B1, and A1FI has projected global rise in atmospheric carbon dioxide. The data contained a resolution of 3.75×2.5 for three 30-year average periods. These periods were 2020s, 2050s and 2080s. These scenarios were then represented in storylines. The storylines represented mutually consistent characterizations of future countries of the world within the 21st century. This included economic and demographic development and the related alterations in climate and level of the sea. The gross domestic product and the related population scenarios were not constituted within the model. The settings were neither forecasts nor predictions of the future conditions. Instead, they gave a description of the alternative, plausible futures which confirmed to circumstances sets or constraints within which they occurred. The true objective of the scenarios was to get rid of uncertainty by establishing the possible climate change ramifications along a single or very plausible path (Fisher, 1996; Thomson, & Connor, 2001).
The Model
In the derivation of criteria suitability for the model, there was climatic data extraction from approximately fifteen sites in which seasonal profiles of published malaria cases existed. The settings of transmission ranged from holoendmic to malaria free. After this, there was systematic analysis of the site-specific climatic data. This was to aid in the identification of climatic thresholds within the explanation of the seasonal profiles observed. All the thresholds utilized were obtained from printed biological ranges influencing both parasite and vector development. The thresholds were later refined using area-specific knowledge concerning the seasonality and distribution of malaria in Africa. Additionally, it also contained historical unpublished and published maps for Tanzania, Namibia, South Africa and Kenya. There was clinical set data for the case of Botswana and South Africa (Fisher, 1996).
The variables of two months plus three annual variables made the basis for the model. The two monthly variables represented the moving mean rainfall and temperatures. On the other hand, the three monthly annual variables represented the least temperature, average monthly temperature standard deviation. It also represented the availability of a catalyst month. Because a sporadic month, of appropriate climatic conditions is not enough for transmission of malaria, a three month moving average was used. This was in relation to the variables like temperature and rainfall. Hence, March, the figures utilized were the means of the first three months. As soon as the lowest temperature approached freezing point, the anopheline vector populations in Africa reduced radically. At a temperature that was consistent at 19.5 degrees Celsius, the length of the Plasmodium falciparum sporagonic cycle was approximately 32 days alongside 4 percent of the whole vector cohort existence.
There was the analysis of stable and seasonal profiles of climate. It showed that decreased monthly temperatures would sustain malaria transmission in malarious stable zones. The distinctions were a function of the yearly temperature variations. Within seasonal areas, parasite and vector populations required to be completely regenerated after the winter cold months in order to enhance transmission. The seasonal areas represent high altitudes and latitudes. Within the stable areas, temperatures hovered around the threshold levels for a long period of the year. Hence, decreased temperatures could sustain transmission on the basis of the prevailing reservoir for the parasite. The criteria utilized for monthly calculations for P. falciparum transmission of malaria in African studies of the anopheline mosquitoes indicate a close relationship between precipitation and the presence of breeding sites (Bremen, 2009; Bruce, 1985).
In addition, rainfall was closely associated with the status of soil moisture. This is a very significant factor in the survival of mosquito. On the contrary, a reasonable lag may exist between sustainable soil moisture and the event of precipitation. Appropriate breeding sites for vectors could take place in a region which has recorded no rainfall or little rain for the present month on the strength following events of precipitation. On the other hand, the values of latent moisture are likely to be lowered during a mean rainfall month. However, this is normally followed by little conditions of rainfall. Hence, a tri-month moving mean was also utilized for the precipitation data. The approach made possible for rainfall from the past two months to participate to an accurate status of moisture estimate in the present month. The analysis of different profiles of the climate in both stable and seasonal areas of malaria has illustrated the requirement for a stimulus month. This constituted unpublished data. It also represented a month with a lot of suitable conditions of rainfall to give enough breeding sites for vectors and regenerate the population of the vectors. It was appropriate to the analysis of vector-borne disease within the climate variability context. There was a continued analysis that that represented an essential new chance for the community of malaria. This should elaborate the research questions which would be likely to be addressed by the new services.
The one-month prediction transmission interruption was given status of transmission on the strength of the suitability of climate for the bordering months and the prevailing reservoir of parasites. These thresholds are made to delimit high-incidence areas of malaria because of the utilization of extended average data precluding the delimitation of epidemic areas occasionally. All the criteria had to be reached for a pixel to be categorized as malarious in a given month.
Model validation
There was the analysis of 6284 laboratory confirmed surveys of parasite ratio constituting approximately one million tested people within the African continent. These figures were taken between 1929 and 1994. The selection was based on surveys done during the first month. It excluded those which were carried out in the similar location during the same month to lower potential bias as a result of the extra spatiotemporal clustering. Then, there was superimposition of the remaining 3791 surveys, on the seasonal maps which resulted. All these were obtained from the model. It was then, calculated on the basis of specificity and sensitivity of the model within the temporary accuracy of the month. The respective specificity and sensitivity was given a definition as the survey promotions in which occurrence of malaria or non-occurrence of malaria is well predicted by the model within the duration of one month (Pampana, 1969).
Study Design
There was the analysis of 6284 laboratory confirmed surveys of parasite ratio constituting approximately one million tested people within the African continent.
These figures were taken between 1929 and 1994. The selection was based on surveys done during the first month. It excluded those which were carried out in the similar location during the same month to lower potential bias as a result of the extra spatiotemporal clustering. Then, there was superimposition of the remaining 3791 surveys, on the seasonal maps which resulted. All these were obtained from the model. It was then, calculated on the basis of specificity and sensitivity of the model within the temporary accuracy of the month. The respective specificity and sensitivity was given a definition as the survey promotions in which occurrence of malaria or non-occurrence of malaria is well predicted by the model within the duration of one month (Pampana, 1969; Sloof, 1961).
Mechanism of Exposure
There exists a prediction that there will be an increase in malaria, in the whole world, as the tropical areas are warm, thus, having widespread cases of malaria. Hence, warm areas will experience many malaria incidences in the future. As a result of the existing standard practice, there was interpolation of the future surfaces of different climatic scenarios. This was towards the resolution of the extended average data utilizing a bilinear interpolation. Change data was superimposed from the HadCM3 experiments onto the extended average data to spread projections for future periods. There was also superimposition of the gridded population’s data on the outcome maps in the calculation of the approximate person-months of exposure within present and future conditions of the climate. To separate the impacts of global warming, there was an assumption that there was a constant population within the century, and there was no attempt to combine projections of the population into future estimates (Boyd, 1949).
There was the use of personal-months of risk as the major result measure for assessing the whole possible impacts of climate on transmission of malaria. The measure incorporated both the temporal population aspects and standard aspects of population exposure (World Health Organization, 1999).
Identified and Potential Genetic & Environmental Interactions
Malaria is extremely sensitive to certain climatic conditions. Hence, thresholds of temperatures reduce the geographical ranges of mosquitoes. A good example is the extreme heat which kills mosquitoes. On the contrary, warmer temperatures within their ranges of survival increase the reproduction rate of mosquitoes, activity of biting and the rate of maturity of pathogens within them. Anopheles mosquitoes are the vectors for the malaria parasite. They only survive a few weeks. The transmission of malaria by the mosquitoes occurs when temperatures go beyond sixteen degrees Celsius. The extreme sensitivity towards seasonal patterns gives an explanation on how precipitation can make the breeding sites for mosquitoes to increase while dry periods eliminating the breeding sites. On the contrary, the same creates a new habitat for the mosquitoes (Epstein, 2001; Macdonald, 1957).
There is a prediction that climate change will result into a rise in the transmission of malaria. The evidence for this is that there are many areas in which there were no cases of malaria. Currently, these areas record high cases of malaria transmission. The areas are now experiencing very hot conditions of weather. From the time, the mean temperatures of the earth increased by a single degree, vectors and vector-borne diseases are moving towards high altitudes (Epstein, 2001; White, 2008).
Epidemiology of Outcomes
There exists a general proposition for a method to identify the indirect mechanisms and interactions, establish the various gaps of research and incorporate the research to perfect comprehension of the whole system of global warming and vector-borne diseases. Global warming causes ecological alterations like population change and range of a given species. Additionally, it brings about social changes. As a result, it affects epidemiological results like birth and death rates. Hence, the combination between the sociological, ecological, biological and epidemiological is the perfect way to come up with an incorporated assessment framework for examining research concerning the relationship between transmission of malaria and climate change. The changes in the ecology include loss of biodiversity, relocation of communities and changes in the availability of nutrients (Kiszewski, & Teklehaimanot, 2004; Stuckenberg, 1969).
The diagram provides a link between global warming and malaria transmission. It illustrates various disease determinants, hence, making it hard for study. Vector-borne diseases and ecological changes study debate habitat degradation. On the contrary, sociological changes and vector-borne diseases study deal with economic developments like sanitation and nutrition (Chan et al., 1999; Hammond, 1996).
Studies on vector-borne diseases like malaria and ecological changes describe the destruction of the habitat. It also describes possible global warming effects on water supplies and food. It also affects resource availability effects on demographic changes of human beings such as migration and urbanization. Additionally, there are other confounding travel effects and pollution and loss of habitat.
Data Analysis
The seasonality model estimated that averagely, there are approximately 3.1 billion person-months of malaria exposure. This implies that around 445 million people in Africa are exposed to malaria annually. The spatial and temporal validation of the proposed present distribution of malaria was carried out with positive (n=3199) and negative (n=592) surveys of the parasite in one month duration. The model illustrated a particular sensitivity. This was the capacity of the model to predict zones of transmission accurately in a spawn of one month. Specificity in the single month had temporal accuracy of 96 %. The specificity was very remarkable because surveys of malaria are normally carried out in zones and during periods of the year when cases of malaria are anticipated to have been documented previously. In the same way, the sensitivity is obtained using some surveys carried out during years of epidemic and in zones with permanent breeding sites. When small area and inter-annual variation and effect of vector mitigation and data resolution are considered, the whole accuracy becomes perfect (Lafferty, 2009).
Conclusion
Global warming is taking place and it is affecting biological, epidemiological, ecological, and sociological systems which the life of human beings depend. On the contrary, the level of this effect is unknown because the various are not only numerous but also interrelated (Partz et al, 2004). Additionally, the inability of the models of global circulation to predict the present climate from retrospective data accurately has resulted to heated debate concerning their application. As our comprehension of the dynamics of global climate rise, models are highly capable of handling the complexity. Hence, the resurgence of highland malaria cannot be attributed entirely to the current climate change. A recent study in some highland areas in Africa, showed no big climate change during resurgence. Some of the incidences were attributed to some conditions like breakdown of control programs, drug resistance and change in land use. The climatic condition variations like rainfall patterns, temperature and humidity, contain a serious impact on the longevity of mosquito. Additionally, it contains an impact on the development of the parasite of malaria within the mosquito. This, in turn, affects the transmission of malaria. The temperatures of the world have increased steadily over the last century. This has been coupled with an increased warming trend for the last sixty years. The research, thus gives a suggestion that the rise will facilitate the rates of transmission of malaria and broaden its distribution in the world (Hammond, 1996).
Budget
Timeline/Tentative Schedule
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