Identifying areas with a high risk of human infection with the avian influenza A (H7N9) virus in East Asia

ObjectivesThe rapid emergence, spread, and disease severity of avian influenza A (H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future human infections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors.MethodsThis cross-sectional study used the locations of H7N9 cases and negative cases from China's influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia.ResultsLive bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus' spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China.ConclusionsOur risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases

Monitoring Avian Influenza A(H7N9) Virus through National Influenza-like Illness Surveillance, China

In China during March 4–April 28, 2013, avian influenza A(H7N9) virus testing was performed on 20,739 specimens from patients with influenza-like illness in 10 provinces with confirmed human cases: 6 (0.03%) were positive, and increased numbers of unsubtypeable influenza-positive specimens were not seen. Careful monitoring and rapid characterization of influenza A(H7N9) and other influenza viruses remain critical

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study

BackgroundIn the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. ObjectiveWe aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. MethodsWe estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. ResultsWe estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. ConclusionsOur novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data

Human Influenza A(H7N9) Virus Infection Associated with Poultry Farm, Northeastern China

We report on a case of human infection with influenza A(H7N9) virus in Jilin Province in northeastern China. This case was associated with a poultry farm rather than a live bird market, which may point to a new focus for public health surveillance and interventions in this evolving outbreak

Change in vitamin D level from 0 to 24 weeks in blacks by country*.

<p>*25-OH Vitamin D level measured in ng/ml. Results are from multivariate linear regression analysis controlling for treatment arm, season, baseline 25-OH vitamin D level, CD4, viral load, age, sex and body mass index.</p

Results of regression analyses for change in vitamin D from 0 to 24 weeks^*.

<p>*Abbreviations: CI, confidence interval; BMI, body mass index; β values are unstandardized regression coefficients.</p>1<p>Both treatment arms A and C are compared to treatment arm B.</p>2<p>Reference group is summer/fall (“high vitamin D season”), defined as months 12–5 (southern hemisphere) or 6–11 (northern hemisphere). Comparison group is reciprocal seasons for each hemisphere respectively.</p>3<p>Wald p-value for overall p-value for country in multivariate analyses. For individual countries in multivariate analayses, p-value is compared to reference country Brazil.</p>4<p>Reference group is female.</p>5<p>Reference group has no self-reported history of AIDS prior to study entry.</p>6<p>Wald p-value for overall p-value for race. For individual countries in multivariate analyses, p-value is compared to reference race Asian.</p