Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-3 (of 3 Records) |
Query Trace: Buckee CO[original query] |
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Individual model forecasts can be misleading, but together they are useful.
Buckee CO , Johansson MA . Eur J Epidemiol 2020 35 (8) 1-2 The broad use by media and governments of model forecasts to inform the COVID-19 response has been a prominent and controversial feature of the pandemic so far. In this issue, Chin et al. compare the accuracy of four high profile models that, early during the outbreak in the US, aimed to make quantitative predictions about deaths and Intensive Care Unit (ICU) bed utilization in New York [1]. They find that all four models, though different in approach, failed not only to accurately predict the number of deaths and ICU utilization but also to describe uncertainty appropriately, particularly during the critical early phase of the epidemic. While overcoming these methodological challenges is key, Chin et al. also call for systemic advances including improving data quality, evaluating forecasts in real-time before policy use, and developing multi-model approaches. |
Impact of human mobility on the emergence of dengue epidemics in Pakistan
Wesolowski A , Qureshi T , Boni MF , Sundsoy PR , Johansson MA , Rasheed SB , Engo-Monsen K , Buckee CO . Proc Natl Acad Sci U S A 2015 112 (38) 11887-92 The recent emergence of dengue viruses into new susceptible human populations throughout Asia and the Middle East, driven in part by human travel on both local and global scales, represents a significant global health risk, particularly in areas with changing climatic suitability for the mosquito vector. In Pakistan, dengue has been endemic for decades in the southern port city of Karachi, but large epidemics in the northeast have emerged only since 2011. Pakistan is therefore representative of many countries on the verge of countrywide endemic dengue transmission, where prevention, surveillance, and preparedness are key priorities in previously dengue-free regions. We analyze spatially explicit dengue case data from a large outbreak in Pakistan in 2013 and compare the dynamics of the epidemic to an epidemiological model of dengue virus transmission based on climate and mobility data from approximately 40 million mobile phone subscribers. We find that mobile phone-based mobility estimates predict the geographic spread and timing of epidemics in both recently epidemic and emerging locations. We combine transmission suitability maps with estimates of seasonal dengue virus importation to generate fine-scale dynamic risk maps with direct application to dengue containment and epidemic preparedness. |
Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones
Wesolowski A , Stresman G , Eagle N , Stevenson J , Owaga C , Marube E , Bousema T , Drakeley C , Cox J , Buckee CO . Sci Rep 2014 4 5678 Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases. |
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