Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-2 (of 2 Records) |
Query Trace: DeVita T[original query] |
---|
Notes from the field: Locally acquired mosquito-transmitted (autochthonous) plasmodium falciparum malaria - national capital region, Maryland, August 2023
Duwell M , DeVita T , Torpey D , Chen J , Myers RA , Mace K , Ridpath AD , Odongo W , Raphael BH , Lenhart A , Tongren JE , Stanley S , Blythe D . MMWR Morb Mortal Wkly Rep 2023 72 (41) 1123-1125 Although malaria was eliminated in the United States in the mid-1950s, approximately 2,000 malaria cases are imported into the United States from regions with endemic disease transmission each year, including approximately 200 in Maryland* (Figure) (1). Anopheles mosquito species that can transmit malaria exist in many areas in the United States (2). Locally acquired mosquito-transmitted (i.e., autochthonous) cases have not been identified since 2003; however, these imported cases represent a potential source of infection. In mid-2023, eight autochthonous malaria cases (Plasmodium vivax) were identified in Florida and Texas (3); in both states, the autochthonous cases occurred in the vicinity of an imported malaria case. |
An open challenge to advance probabilistic forecasting for dengue epidemics.
Johansson MA , Apfeldorf KM , Dobson S , Devita J , Buczak AL , Baugher B , Moniz LJ , Bagley T , Babin SM , Guven E , Yamana TK , Shaman J , Moschou T , Lothian N , Lane A , Osborne G , Jiang G , Brooks LC , Farrow DC , Hyun S , Tibshirani RJ , Rosenfeld R , Lessler J , Reich NG , Cummings DAT , Lauer SA , Moore SM , Clapham HE , Lowe R , Bailey TC , Garcia-Diez M , Carvalho MS , Rodo X , Sardar T , Paul R , Ray EL , Sakrejda K , Brown AC , Meng X , Osoba O , Vardavas R , Manheim D , Moore M , Rao DM , Porco TC , Ackley S , Liu F , Worden L , Convertino M , Liu Y , Reddy A , Ortiz E , Rivero J , Brito H , Juarrero A , Johnson LR , Gramacy RB , Cohen JM , Mordecai EA , Murdock CC , Rohr JR , Ryan SJ , Stewart-Ibarra AM , Weikel DP , Jutla A , Khan R , Poultney M , Colwell RR , Rivera-Garcia B , Barker CM , Bell JE , Biggerstaff M , Swerdlow D , Mier YTeran-Romero L , Forshey BM , Trtanj J , Asher J , Clay M , Margolis HS , Hebbeler AM , George D , Chretien JP . Proc Natl Acad Sci U S A 2019 116 (48) 24268-24274 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. |
- Page last reviewed:Feb 1, 2024
- Page last updated:May 06, 2024
- Content source:
- Powered by CDC PHGKB Infrastructure