<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Megacities as drivers of national outbreaks: The 2017 chikungunya outbreak in Dhaka, Bangladesh."
Abstract:"Author summary Chikungunya is an emerging mosquito-borne disease in many parts of the world, with a high morbidity burden. Fluctuations in human density and mobility are important drivers of epidemics, particularly in the context of large cities in low- and middle-income countries, which can act to amplify and spread local epidemics. Here, we first describe the epidemiology of chikungunya in Dhaka, Bangladesh, one of the largest megacities in the world, during an outbreak in 2017. Using data from a household survey, we estimate a much higher attack rate than suggested by official case counts. We then use estimates of population movement from mobile phone data for over 4 million subscribers, to understand the role of human mobility on the spatial spread of chikungunya. We show that regular population fluxes around Dhaka city played a significant role in determining disease risk, and that travel during the Eid holidays likely spread the infection to the rest of the country. Our results show the impact of large-scale population movements, particularly during holidays, on the spread of infectious diseases, and highlight the value of real-time data from mobile phones for outbreak analysis and forecasting. Background Several large outbreaks of chikungunya have been reported in the Indian Ocean region in the last decade. In 2017, an outbreak occurred in Dhaka, Bangladesh, one of the largest and densest megacities in the world. Population mobility and fluctuations in population density are important drivers of epidemics. Measuring population mobility during outbreaks is challenging but is a particularly important goal in the context of rapidly growing and highly connected cities in low- and middle-income countries, which can act to amplify and spread local epidemics nationally and internationally. Methods We first describe the epidemiology of the 2017 chikungunya outbreak in Dhaka and estimate incidence using a mechanistic model of chikungunya transmission parametrized with epidemiological data from a household survey. We combine the modeled dynamics of chikungunya in Dhaka, with mobility estimates derived from mobile phone data for over 4 million subscribers, to understand the role of population mobility on the spatial spread of chikungunya within and outside Dhaka during the 2017 outbreak. Results We estimate a much higher incidence of chikungunya in Dhaka than suggested by official case counts. Vector abundance, local demographics, and population mobility were associated with spatial heterogeneities in incidence in Dhaka. The peak of the outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to other parts of the country. We show that travel during Eid likely resulted in the spread of the infection to the rest of the country. Conclusions Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are d."
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Megacities as drivers of national outbreaks: The 2017 chikungunya outbreak in Dhaka, Bangladesh
Author summary Chikungunya is an emerging mosquito-borne disease in many parts of the world, with a high morbidity burden. Fluctuations in human density and mobility are important drivers of epidemics, particularly in the context of large cities in low- and middle-income countries, which can act to amplify and spread local epidemics. Here, we first describe the epidemiology of chikungunya in Dhaka, Bangladesh, one of the largest megacities in the world, during an outbreak in 2017. Using data from a household survey, we estimate a much higher attack rate than suggested by official case counts. We then use estimates of population movement from mobile phone data for over 4 million subscribers, to understand the role of human mobility on the spatial spread of chikungunya. We show that regular population fluxes around Dhaka city played a significant role in determining disease risk, and that travel during the Eid holidays likely spread the infection to the rest of the country. Our results show the impact of large-scale population movements, particularly during holidays, on the spread of infectious diseases, and highlight the value of real-time data from mobile phones for outbreak analysis and forecasting. Background Several large outbreaks of chikungunya have been reported in the Indian Ocean region in the last decade. In 2017, an outbreak occurred in Dhaka, Bangladesh, one of the largest and densest megacities in the world. Population mobility and fluctuations in population density are important drivers of epidemics. Measuring population mobility during outbreaks is challenging but is a particularly important goal in the context of rapidly growing and highly connected cities in low- and middle-income countries, which can act to amplify and spread local epidemics nationally and internationally. Methods We first describe the epidemiology of the 2017 chikungunya outbreak in Dhaka and estimate incidence using a mechanistic model of chikungunya transmission parametrized with epidemiological data from a household survey. We combine the modeled dynamics of chikungunya in Dhaka, with mobility estimates derived from mobile phone data for over 4 million subscribers, to understand the role of population mobility on the spatial spread of chikungunya within and outside Dhaka during the 2017 outbreak. Results We estimate a much higher incidence of chikungunya in Dhaka than suggested by official case counts. Vector abundance, local demographics, and population mobility were associated with spatial heterogeneities in incidence in Dhaka. The peak of the outbreak in Dhaka coincided with the annual Eid holidays, during which large numbers of people traveled from Dhaka to other parts of the country. We show that travel during Eid likely resulted in the spread of the infection to the rest of the country. Conclusions Our results highlight the impact of large-scale population movements, for example during holidays, on the spread of infectious diseases. These dynamics are difficult to capture using traditional approaches, and we compare our results to a standard diffusion model, to highlight the value of real-time data from mobile phones for outbreak analysis, forecasting, and surveillance.