2010
DOI: 10.1007/s11356-010-0388-x
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Potential malaria outbreak in Germany due to climate warming: risk modelling based on temperature measurements and regional climate models

Abstract: The presented risk prognosis is based on the R (0) formula for the estimation of the reproduction of the malaria pathogen Plasmodium vivax. The presented model focuses on mean air temperatures; thus, other driving factors like the distribution of water bodies (breeding habitats) or population density are not integrated. Nevertheless, the modelling presented in this study can help identify areas at risk and initiate prevention. The described findings may also help in the investigation and assessment of related … Show more

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Cited by 18 publications
(8 citation statements)
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“…Other common types of risk maps included clinical case incidence (n = 21, 19%) and vector density or breeding sites (n = 14, 13%). Less commonly, risk was mapped according to estimated values of metrics such as the basic reproductive number R 0 [ 25 ], the entomological inoculation rate (EIR) [ 15 , 26 , 27 ], and malariogenic potential [ 28 ] (the risk of malaria transmission, determined by importation and transmission potential [ 29 ]); Noor et al mapped historical levels of prevalence as proxies for the baseline risk that would exist in the absence of control measures in Namibia [ 30 ], Somalia [ 31 ], and Sudan [ 32 ].…”
Section: Malaria Risk Mapping In Practicementioning
confidence: 99%
“…Other common types of risk maps included clinical case incidence (n = 21, 19%) and vector density or breeding sites (n = 14, 13%). Less commonly, risk was mapped according to estimated values of metrics such as the basic reproductive number R 0 [ 25 ], the entomological inoculation rate (EIR) [ 15 , 26 , 27 ], and malariogenic potential [ 28 ] (the risk of malaria transmission, determined by importation and transmission potential [ 29 ]); Noor et al mapped historical levels of prevalence as proxies for the baseline risk that would exist in the absence of control measures in Namibia [ 30 ], Somalia [ 31 ], and Sudan [ 32 ].…”
Section: Malaria Risk Mapping In Practicementioning
confidence: 99%
“…Current antimalarial interventions lead to a reduction in the basic rate of malaria reproduction by reducing human infectivity with early and effective treatment and reducing vector capacity by mos-quito control measures [40]. However, when taking into account such changes of environmental and social patterns, such as increased migration to cities with subsequent increase in health conditions, climate change and shifting disease patterns, wider access to wireless technologies (cellular phones and the Internet), innovative surveillance and response are also highly accessible in remote areas where people live under poor conditions [30,41]. The capacity of surveillance and response is fuelled by heightened awareness of the importance of national core capacities for surveillance and response demonstrated by adoption of the International Health Regulations [42].…”
Section: The Role Of Surveillance and Response In Disease Eliminationmentioning
confidence: 99%
“…The changes of average temperature, extreme weather events, such as drought and heavy precipitation, and the duration of seasons also affect the geographic distribution and intensity of transmission of infectious diseases [24]. People in developing countries, particularly in tropical areas, are likely to suffer most from climate change due to poverty, poor sanitation, poor health status, high population density, poor health care systems, and political instability that are influenced by the limited ability of health care systems to respond to an increase in the burden of climate-sensitive outcomes [5].…”
Section: Introductionmentioning
confidence: 99%