ABSTRACT
Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.
Subject(s)
COVID-19 , Forecasting , Military Personnel , Population Surveillance , Humans , COVID-19/epidemiology , Forecasting/methods , United States/epidemiology , Military Personnel/statistics & numerical data , Population Surveillance/methods , SARS-CoV-2 , Influenza, Human/epidemiology , Models, Statistical , Male , Respiratory Tract Infections/epidemiology , FemaleABSTRACT
A retrospective study of Afghanistan National Army casualty rates for a 1-year period was completed to assist in health care system assessment and further development during a period of rapid force expansion. Battle and disease nonbattle injuries by Corps area were determined from data on soldier visits from all military health care facilities. The number of fielded forces in each Corps was used to calculate the populations at risk. Total manpower losses from all casualties were tabulated. The 15,336 casualties (175 per thousand fielded soldiers) resulted in the loss of 146,986 duty days (average 9.5 days per casualty). Battle casualties were 739 (8.4 per 1,000) and nonbattle casualties were 14,597(166 per 1,000) with 72% secondary to infectious diseases. Casualty rates from both battle and disease nonbattle injuries were high, but casualty rates were particularly high from infectious diseases. Rapid force expansion in developing countries requires early consideration for resourcing and implementation of preventive medicine programs.
