Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010–2014

Lauer, Stephen A.; Sakrejda, Krzysztof; Ray, Evan L.; Keegan, Lindsay T; Bi, Qifang; Suangtho, Paphanij; Hinjoy, Soawapak; Iamsirithaworn, Sopon; Suthachana, Suthanun; Laosiritaworn, Yongjua; Cummings, Derek A. T.; Lessler, Justin; Reich, Nicholas G.

doi:10.1073/pnas.1714457115

Cited by 59 publications

(76 citation statements)

References 44 publications

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“…Poisson 95% prediction intervals were estimated by sampling from the uncertainty distributions for the estimated model parameters. 14 To calculate the number of COVID-19 related excess deaths, we subtracted the expected number of deaths in each week from the observed number of deaths for the period February 9, 2020 to March 28, 2020. Because reporting of deaths for recent weeks is incomplete, NCHS calculates a 'completeness' score (between 0 and 1) based on the number of death reports that have been received from a state and the number expected from that state based on previous years.…”

Section: Excess Mortality and Morbidity Analysismentioning

confidence: 99%

Estimating the early death toll of COVID-19 in the United States

Weinberger¹,

Cohen²,

Crawford

et al. 2020

Preprint

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Background Efforts to track the severity and public health impact of the novel coronavirus, COVID-19, in the US have been hampered by testing issues, reporting lags, and inconsistency between states. Evaluating unexplained increases in deaths attributed to broad outcomes, such as pneumonia and influenza (P&I) or all causes, can provide a more complete and consistent picture of the burden caused by COVID-19. Methods We evaluated increases in the occurrence of deaths due to P&I above a seasonal baseline (adjusted for influenza activity) or due to any cause across the United States in February and March 2020. These estimates are compared with reported deaths due to COVID-19 and with testing data. Results There were notable increases in the rate of death due to P&I in February and March 2020. In a number of states, these deaths pre-dated increases in COVID-19 testing rates and were not counted in official records as related to COVID-19. There was substantial variability between states in the discrepancy between reported rates of death due to COVID-19 and the estimated burden of excess deaths due to P&I. The increase in all-cause deaths in New York and New Jersey is 1.5-3 times higher than the official tally of COVID-19 confirmed deaths or the estimated excess death due to P&I. Conclusions Excess P&I deaths provide a conservative estimate of COVID-19 burden and indicate that COVID-19-related deaths are missed in locations with inadequate testing or intense pandemic activity.

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Section: Excess Mortality and Morbidity Analysismentioning

confidence: 99%

Estimating the early death toll of COVID-19 in the United States

Weinberger¹,

Cohen²,

Crawford

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result,

E [Y | X]

can be estimated as

g^{- 1} ({\hat{f}}_{0} + \sum_{j} {\hat{f}}_{j} (X_{j}))

. Because the GAM is very flexible in describing the dependence of the response on the covariates, it has been widely used as a prediction model in statistical applications …”

Section: Phase I Modelingmentioning

confidence: 99%

“…In the first step, we develop a generalized additive model (GAM) based on the weekly count data from 2006 to 2011. The GAM is widely recognized as a flexible prediction model and its applications can be found in a variety of areas such as ecology, energy, and bio‐surveillance systems . The proposed forecasting procedure is novel in the following three aspects.…”

Section: Introductionmentioning

confidence: 99%

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

Chen

et al. 2020

Statistics in Medicine

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Dengue has been as an endemic with year‐round presence in Singapore. In the recent years 2013, 2014, and 2016, there were several severe dengue outbreaks, posing serious threat to the public health. To proactively control and mitigate the disease spread, early warnings of dengue outbreaks, at which there are rapid and large‐scale spread of dengue incidences, are extremely helpful. In this study, a two‐step framework is proposed to predict dengue outbreaks and it is evaluated based on the dengue incidences in Singapore during 2012 to 2017. First, a generalized additive model (GAM) is trained based on the weekly dengue incidence data during 2006 to 2011. The proposed GAM is a one‐week‐ahead forecasting model, and it inherently accounts for the possible correlation among the historical incidence data, making the residuals approximately normally distributed. Then, an exponentially weighted moving average (EWMA) control chart is proposed to sequentially monitor the weekly residuals during 2012 to 2017. Our investigation shows that the proposed two‐step framework is able to give persistent signals at the early stage of the outbreaks in 2013, 2014, and 2016, which provides early alerts of outbreaks and wins time for the early interventions and the preparation of necessary public health resources. In addition, extensive simulations show that the proposed method is comparable to other potential outbreak detection methods and it is robust to the underlying data‐generating mechanisms.

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“…For developing forecast systems, this feature implies a trade-off between model consistency and spatial resolution. As a consequence, most studies to date focus on producing ad-hoc predictions for a single location, ranging from the national-to the city-level [26][27][28] , while others build and evaluate multiple modeling strategies per study site in efforts to manually identify relationships between weather patterns and dengue incidence over different geographies and temporal windows 29,30 . Both approaches highlight the difficulty in producing forecast models that are viable in diverse settings.…”

Section: Introductionmentioning

confidence: 99%

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

et al. 2019

Preprint

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Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito dynamics, which often change in time and space. It is well known that disease dynamics are highly influenced by a population's susceptibility to infection and microclimates, small-area climatic conditions which create environments favorable for the breeding and survival of the mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology, it may prove valuable for public-health decision making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.

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Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010–2014

Cited by 59 publications

References 44 publications

Estimating the early death toll of COVID-19 in the United States

Estimating the early death toll of COVID-19 in the United States

Early dengue outbreak detection modeling based on dengue incidences in Singapore during 2012 to 2017

Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach

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