Browsing by Author "Stewart-Ibarra, Anna M."
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- Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic modelsMordecai, Erin A.; Cohen, Jeremy M.; Evans, Michelle V.; Gudapati, Prithvi; Johnson, Leah R.; Lippi, Catherine A.; Miazgowicz, Kerri; Murdock, Courtney C.; Rohr, Jason R.; Ryan, Sadie J.; Savage, Van; Shocket, Marta S.; Stewart-Ibarra, Anna M.; Thomas, Matthew B.; Weikel, Daniel P. (PLOS, 2017-04)Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34 degrees C with maximal transmission occurring in a range from 26-29 degrees C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.
- Increasing arbovirus risk in Chile and neighboring countries in the Southern Cone of South AmericaEollo, Elizabet L.; Sippy, Rachel; Robert, Michael A.; Ayala, Salvador; Pizard, Carlos J. Barboza; Perez-Estigarribia, Pastor E.; Stewart-Ibarra, Anna M. (Elsevier, 2023-06-23)
- An open challenge to advance probabilistic forecasting for dengue epidemicsJohansson, Michael A.; Apfeldorf, Karyn M.; Dobson, Scott; Devita, Jason; Buczak, Anna L.; Baugher, Benjamin; Moniz, Linda J.; Bagley, Thomas; Babin, Steven M.; Guven, Erhan; Yamana, Teresa K.; Shaman, Jeffrey; Moschou, Terry; Lothian, Nick; Lane, Aaron; Osborne, Grant; Jiang, Gao; Brooks, Logan C.; Farrow, David C.; Hyun, Sangwon; Tibshirani, Ryan J.; Rosenfeld, Roni; Lessler, Justin; Reich, Nicholas G.; Cummings, Derek AT T.; Lauer, Stephen A.; Moore, Sean M.; Clapham, Hannah E.; Lowe, Rachel; Bailey, Trevor C.; Garcia-Diez, Markel; Carvalho, Marilia Sa; Rodo, Xavier; Sardar, Tridip; Paul, Richard; Ray, Evan L.; Sakrejda, Krzysztof; Brown, Alexandria C.; Meng, Xi; Osoba, Osonde; Vardavas, Raffaele; Manheim, David; Moore, Melinda; Rao, Dhananjai M.; Porco, Travis C.; Ackley, Sarah; Liu, Fengchen; Worden, Lee; Convertino, Matteo; Liu, Yang; Reddy, Abraham; Ortiz, Eloy; Rivero, Jorge; Brito, Humberto; Juarrero, Alicia; Johnson, Leah R.; Gramacy, Robert B.; Cohen, Jeremy M.; Mordecai, Erin A.; Murdock, Courtney C.; Rohr, Jason R.; Ryan, Sadie J.; Stewart-Ibarra, Anna M.; Weikel, Daniel P.; Jutla, Antarpreet; Khan, Rakibul; Poultney, Marissa; Colwell, Rita R.; Rivera-Garcia, Brenda; Barker, Christopher M.; Bell, Jesse E.; Biggerstaff, Matthew; Swerdlow, David; Mier-y-Teran-Romero, Luis; Forshey, Brett M.; Trtanj, Juli; Asher, Jason; Clay, Matt; Margolis, Harold S.; Hebbeler, Andrew M.; George, Dylan; Chretien, Jean-Paul (National Academy of Sciences, 2019-11-26)A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.
- Spatiotemporal Variation in Environmental Vibrio cholerae in an Estuary in Southern Coastal EcuadorRyan, Sadie J.; Stewart-Ibarra, Anna M.; Ordóñez-Enireb, Eunice; Chu, Winnie; Finkelstein, Julia L.; King, Christine A.; Escobar, Luis E.; Lupone, Christina; Heras, Froilan; Tauzer, Erica; Waggoner, Egan; James, Tyler G.; Cárdenas, Washington B.; Polhemus, Mark (MDPI, 2018-03-10)Cholera emergence is strongly linked to local environmental and ecological context. The 1991–2004 pandemic emerged in Perú and spread north into Ecuador’s El Oro province, making this a key site for potential re-emergence. Machala, El Oro, is a port city of 250,000 inhabitants, near the Peruvian border. Many livelihoods depend on the estuarine system, from fishing for subsistence and trade, to domestic water use. In 2014, we conducted biweekly sampling for 10 months in five estuarine locations, across a gradient of human use, and ranging from inland to ocean. We measured water-specific environmental variables implicated in cholera growth and persistence: pH, temperature, salinity, and algal concentration, and evaluated samples in five months for pathogenic and non-pathogenic Vibrio cholerae, by polymerase chain reaction (PCR). We found environmental persistence of pandemic strains O1 and O139, but no evidence for toxigenic strains. Vibrio cholerae presence was coupled to algal and salinity concentration, and sites exhibited considerable seasonal and spatial heterogeneity. This study indicates that environmental conditions in Machala are optimal for cholera re-emergence, with risk peaking during September, and higher risk near urban periphery low-income communities. This highlights a need for surveillance of this coupled cholera–estuarine system to anticipate potential future cholera outbreaks.