Dengue is a vector-borne disease and a major public health concern in Brazil.Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and re...Dengue is a vector-borne disease and a major public health concern in Brazil.Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and response to the disease.In this context,we propose a Bayesian forecasting model based on historical data to predict the number of cases 52 weeks ahead for the 118 health districts of Brazil.We leverage the predictions to build probabilistic epidemics bands to be used for dengue monitoring.We define four disjoint probabilistic bands(≤50%(50%,75%](75%,90%],and>90%),based on the percentiles of the predicted cases distribution and interpreted according to the historical number of cases and past occurrence probability(below the median,typical;moderately high,fairly typical;fairly high,atypical;exceptionally high,very atypical).We performed out-of-sample validation for 2022–2023 and 2023–2024 and forecasted 2024–2025.In the 2022–2023 and 2023–2024 seasons,the epidemic bands followed the observed cases’curve shape,with a sharp increase after January and a decline after the peak around April.In 2022–2023,the observed number of cases(1,436,034)was slightly above the estimated 75%percentile(1,405,191),being classified as“fairly high,atypical”.Most health districts in South Brazil showed exceptionally high numbers of cases during this season.The situation worsened in 2023–2024 and the observed number of cases(6,454,020)was way above the 90%percentile(2,221,557),characterising an“exceptionally high,very atypical”season.For the 2024–2025 season,we estimated a median number of cases of 1,526,523(maximum value for the“below the median,typical”probabilistic epidemic band.The maximum estimated values for the upper bands were 2,213,282(moderately high,fairly typical)and 3,803,898(fairly high,atypical)with the upper limits of the probabilistic epidemic bands of 1,452,359.Probabilistic epidemic bands serve as a valuable monitoring tool by enabling prospective comparisons between observed case curves and historical epidemic patterns,facilitating the assessment of ongoing outbreaks about past occurrences.展开更多
基金LPF and LSB are supported by a grant from the Inova/Fiocruz/Oswaldo Cruz Foundation,Brazil and the Department of Public Health Emergencies of the Secretariat for Health and Environmental Surveillance of the Ministry of Health(DEMSP/SVSA/MS)-Brazil[VPPCB-002-FIO-20-2-27]by the National Council for Scientific and Technological Development(CNPq),Brazil and the Department of Science and Technology of Secretariat of Science,Technology,Innovation and Health Complex of the Ministry of Health of Brazil(Decit/SECTICS/MS)-Brazil[444896/2023-6]+5 种基金DACF is supported by CNPq-Brazil[200453/2024-6]RML was funded by European Union(Marie Sklodowska-Curie Actions)[grant agreement 101109642]MSC acknowledges support from CNPq-Brazil[307450/2021-0]Fundação Carlos Chagas Filho de AmparoàPesquisa do Estado do Rio de Janeiro(FAPERJ)-Brazil[E−26/203.967/2024]LSB acknowledges support from CNPq-Brazil[310530/2021-0]FAPERJ-Brazil[E−26/201.277/2021 and E−26/204.098/2024].
文摘Dengue is a vector-borne disease and a major public health concern in Brazil.Its continuing and rising burden has led the Brazilian Ministry of Health to request for modelling efforts to aid in the preparedness and response to the disease.In this context,we propose a Bayesian forecasting model based on historical data to predict the number of cases 52 weeks ahead for the 118 health districts of Brazil.We leverage the predictions to build probabilistic epidemics bands to be used for dengue monitoring.We define four disjoint probabilistic bands(≤50%(50%,75%](75%,90%],and>90%),based on the percentiles of the predicted cases distribution and interpreted according to the historical number of cases and past occurrence probability(below the median,typical;moderately high,fairly typical;fairly high,atypical;exceptionally high,very atypical).We performed out-of-sample validation for 2022–2023 and 2023–2024 and forecasted 2024–2025.In the 2022–2023 and 2023–2024 seasons,the epidemic bands followed the observed cases’curve shape,with a sharp increase after January and a decline after the peak around April.In 2022–2023,the observed number of cases(1,436,034)was slightly above the estimated 75%percentile(1,405,191),being classified as“fairly high,atypical”.Most health districts in South Brazil showed exceptionally high numbers of cases during this season.The situation worsened in 2023–2024 and the observed number of cases(6,454,020)was way above the 90%percentile(2,221,557),characterising an“exceptionally high,very atypical”season.For the 2024–2025 season,we estimated a median number of cases of 1,526,523(maximum value for the“below the median,typical”probabilistic epidemic band.The maximum estimated values for the upper bands were 2,213,282(moderately high,fairly typical)and 3,803,898(fairly high,atypical)with the upper limits of the probabilistic epidemic bands of 1,452,359.Probabilistic epidemic bands serve as a valuable monitoring tool by enabling prospective comparisons between observed case curves and historical epidemic patterns,facilitating the assessment of ongoing outbreaks about past occurrences.
