Illustration by Elisa Visher A couple of weeks ago, we held a special early session of the Berkeley EEID seminar so that Professor Ayesha Mahmud from Demography could give a presentation on her research. Ayesha’s research program uses demography methods to study infectious disease spread. At the seminar, she presented her research on the spread of Chikungunya in Dhaka City, Bangladesh. Chikungunya is a viral disease transmitted by Aedes mosquitos that is spreading around the world. It doesn’t cause a large number of fatalities, but can leave people who have been infected, especially the elderly, with long term symptoms. Ayesha focused her talk on work that she has done modelling the 2017 outbreak of Chikungunya in Dhaka City, Bangladesh. Dhaka City is one of the densest megacities in the world with about 18 million people in 1300 sq km. This dense population, along with rapid urbanization, regular monsoons, and almost no vector control, has made Dhaka City susceptible to regular outbreaks of mosquito-borne diseases. There have been frequent dengue outbreaks since 2000 and Chikungunya outbreaks in 2011 and 2017. During the 2017 Chikungunya outbreak in Dhaka, hospitals reported 13,176 cases, but this record is likely an underestimate because hospital reporting was not mandatory. Ayesha therefore based her models on a set of data from collaborators who did household surveys asking about recent symptoms in an effort to get better data on Chikungunya infection rates. They found that 77% of the households surveyed had symptoms, 51% of these individuals had antibodies, and 70% of those had evidence of recent infection. That means that nearly 50% of the population had been recently infected with Chikungunya. With this data, Ayesha and team were able to explore the spatial and temporal heterogeneity in disease burden. This scale of data was especially important as one of the big concerns during the 2017 epidemic was that the outbreak would spread out of Dhaka city into neighboring towns because Eid was right around one of the two peaks in incidence in the city. Using cellphone data, Ayesha was able to track when people were leaving Dhaka city and where they were going. As expected, there were small spikes in city residents travelling over the weekends, with much larger spikes for the Eid holidays. Using a modelling framework with this mobility, demography, and infection data, Ayesha and team were able to make predictions about disease dynamics across the country. The model showed that Chikungunya was likely exported out of Dhaka in a spatial pattern that differed from a standard gravity model as there were certain patterns of travel between different cities in Dhaka. The model did not pick up the double peak in incidence, but the combination of an SEIR and mosquito model did pick up the epidemic curve. The patterns predicted by the model also matched some of the limited reporting from cities outside of Dhaka, which also tended to peak around the Eid holidays. Now, Ayesha is combining these models of case importation risk with metrics like population and mosquito densities in these other cities to predict epidemic risks. They are also looking at data from a Dengue outbreak in Dhaka this year that had much better reporting to see if mobility data can again make some predictions about disease spread. Overall, Ayesha’s talk gave us a great idea about how different types of data can be combined with models to track disease dynamics and impressed upon us how important movement patterns and mass-movement events (like holidays) could be for disease transmission. Summary Elisa Visher
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AuthorBerkeley EEID Group Archives
February 2020
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