The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases

Assessing the global threat from Zika virus.

First discovered in 1947, Zika virus (ZIKV) infection remained a little-known tropical disease until 2015, when its apparent association with a considerable increase in the incidence of microcephaly in Brazil raised alarms worldwide. There is limited information on the key factors that determine the extent of the global threat from ZIKV infection and resulting complications. Here, we review what is known about the epidemiology, natural history, and public health effects of ZIKV infection, the empirical basis for this knowledge, and the critical knowledge gaps that need to be filled

Spatial distribution of leprosy in India : an ecological study

CITATION: Grantz, Kyra H., et al. 2018. Spatial distribution of leprosy in India : an ecological study. Infectious Diseases of Poverty, 7:20, doi:10.1186/s40249-018-0402-y.The original publication is available at https://idpjournal.biomedcentral.comBackground: As leprosy elimination becomes an increasingly realistic goal, it is essential to determine the factors that contribute to its persistence. We evaluate social and economic factors as predictors of leprosy annual new case detection rates within India, where the majority of leprosy cases occur. Methods: We used correlation and linear mixed effect regressions to assess whether poverty, illiteracy, nighttime satellite radiance (an index of development), and other covariates can explain district-wise annual new case detection rate and Grade 2 disability diagnoses. Results: We find only weak evidence of an association between poverty and annual new case detection rates at the district level, though illiteracy and satellite radiance are statistically significant predictors of leprosy at the district level. We find no evidence of rapid decline over the period 2008–2015 in either new case detection or new Grade 2 disability. Conclusions: Our findings suggest a somewhat higher rate of leprosy detection, on average, in poorer districts; the overall effect is weak. The divide between leprosy case detection and true incidence of clinical leprosy complicates these results, particularly given that the detection rate is likely disproportionately lower in impoverished settings. Additional information is needed to distinguish the determinants of leprosy case detection and transmission during the elimination epoch.https://idpjournal.biomedcentral.com/articles/10.1186/s40249-018-0402-yPublisher's versio

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Implications of reinfection for Hepatitis C elimination among people who inject drugs

Background Hepatitis C virus (HCV) causes substantial morbidity and mortality, particularly among people who inject drugs (PWID). The advent of highly effective direct-acting antiviral (DAA) treatments led to calls for HCV elimination. However, as treatments do not provide lasting protection or immunity, reinfection can attenuate the impact of treatment scale-up. There is a need to better understand the risk and occurrence of reinfection, including potential impacts on elimination programs. Objectives Combining statistical and disease transmission models to leverage rich epidemiologic and molecular data from a 30-year cohort of PWID in Baltimore, MD to explore the feasibility of HCV elimination in a high-burden setting. Methods We used random forest and latent class methods to characterize the individual heterogeneity and temporal patterns of HCV infection risk among PWID. We then used long-read next generation sequencing to investigate the relative occurrence of reinfection and treatment failure among PWID receiving DAA treatment. We then built an individual-based transmission model of HCV to generate realistic estimates of the impact of treatment and harm reduction scale-up among PWID in Baltimore. Results We found that 15% of PWID remain at high risk of HCV infection and reinfection for at least 10 years, while others experience transient periods of moderate and low risk. The concentration of reinfection risk and onward transmission among select PWID is projected to reduce the impact of treatment scale-up compared to models which assume equal risk of reinfection. In settings like Baltimore with high HCV prevalence, treatment rates exceeding 90 per 100 person-years may be needed to achieve elimination targets. We also demonstrated the utility of long-read sequencing to recover within-host HCV variants with low error and to distinguish between instances of reinfection and treatment failure. In our small sample, more than two-thirds (18/26) of participants had evidence of treatment failure, indicating the need for integrated, patient-centered care models. Conclusions HCV elimination among PWID will be challenging in high-burden settings like Baltimore, partially due to the high and unevenly-distributed occurrence of reinfection. Substantial investment in treatment, harm reduction, and social services, along with economic and political support, will be needed to achieve elimination targets

Prescribing practices in the treatment of wasting: secondary analysis from a randomised trial

Introduction Current guidelines for the outpatient treatment of severe acute malnutrition (SAM) recommend the provision of routine medications to all children at admission and prescribed medications as clinically indicated thereafter. The objective of this study was to describe the amount and purpose of medications prescribed during outpatient SAM treatment and explore the effect of routine antibiotics at admission on subsequent medication prescription.Methods Medications prescribed during outpatient treatment were described by medication category, time from admission, and diagnoses among children with SAM in a placebo-controlled, double-blind trial of 7-day amoxicillin use. Total medications were compared by parent trial intervention arm (amoxicillin vs placebo) and differences assessed using Χ2 and two-sample t-tests.Results Of the 2399 children enrolled, 74.6% of children received ≥1 prescribed medication during outpatient treatment. Antipyretics/analgesics (44.1% of children), antimalarials (56.6%) and antibiotics (30.0%) were prescribed most frequently. Children who received placebo in the parent trial received fewer total medications (mean difference: −0.80, 95% CI: −0.96 to –0.65) and oral antibiotics (mean difference: −0.96, 95% CI: −0.99 to –0.92) during treatment compared with children who received routine amoxicillin.Conclusions We found high rates of medication prescription during outpatient treatment for SAM, but fewer total medications and oral antibiotics prescribed to children receiving placebo in the parent trial. Our findings underscore the role of outpatient treatment programmes as an important source of medicine prescription and suggest that provision of antibiotics on a clinically indicated basis for outpatient SAM cases may be a strategy to support prudent antibiotic use in certain settings.Trial registration number ClinicalTrials.gov Registry (NCT01613547; https://clinicaltrials.gov/ct2/show/NCT01613547)

Shapefile of census tract boundaries in Chicago in 1920

Shapefile of census tract boundaries in Chicago in 1920. File included in zip file include IL_tract_a.dbf, IL_tract_a.prj, IL_tract_a.sbn, IL_tract_a.sbx, IL_tract_a.shp, IL_tract_a.shp.xml, IL_tract_a.sh