Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most ...healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.
First identified in Wuhan, China, in December 2019, a novel coronavirus (SARS-CoV-2) has affected over 16,800,000 people worldwide as of July 29, 2020 and was declared a pandemic by the World Health ...Organization on March 11, 2020. Influenza studies have shown that influenza viruses survive longer on surfaces or in droplets in cold and dry air, thus increasing the likelihood of subsequent transmission. A similar hypothesis has been postulated for the transmission of COVID-19, the disease caused by SARS-CoV-2. It is important to propose methodologies to understand the effects of environmental factors on this ongoing outbreak to support decision-making pertaining to disease control. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case counts without the implementation of drastic public health interventions.
The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality ...rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.
As of July 15, 2015, the South Korean Ministry of Health and Welfare had reported 186 case-patients with Middle East respiratory syndrome in South Korea. For 159 case-patients with known outcomes and ...complete case histories, we found that older age and preexisting concurrent health conditions were risk factors for death.
ObjectivesAs highlighted by the COVID-19 pandemic, researchers are eager to make use of a wide variety of data sources, both government-sponsored and alternative, to characterise the epidemiology of ...infectious diseases. The objective of this study is to investigate the strengths and limitations of sources currently being used for research.DesignRetrospective descriptive analysis.Primary and secondary outcome measuresYearly number of national-level and state-level disease-specific case counts and disease clusters for three diseases (measles, mumps and varicella) during a 5-year study period (2013–2017) across four different data sources: Optum (health insurance billing claims data), HealthMap (online news surveillance data), Morbidity and Mortality Weekly Reports (official government reports) and National Notifiable Disease Surveillance System (government case surveillance data).ResultsOur study demonstrated drastic differences in reported infectious disease incidence across data sources. When compared with the other three sources of interest, Optum data showed substantially higher, implausible standardised case counts for all three diseases. Although there was some concordance in identified state-level case counts and disease clusters, all four sources identified variations in state-level reporting.ConclusionsResearchers should consider data source limitations when attempting to characterise the epidemiology of infectious diseases. Some data sources, such as billing claims data, may be unsuitable for epidemiological research within the infectious disease context.
Objective
The aim of this study was to examine rates of killings perpetrated by off‐duty police and news coverage of those killings, by victim race and gender, and to qualitatively evaluate the ...contexts in which those killings occur.
Data Sources and Study Setting
We used the Mapping Police Violence database to curate a dataset of killings perpetrated by off‐duty police (2013–2021, N = 242). We obtained data from Media Cloud to assess news coverage of each off‐duty police‐perpetrated killing.
Study Design
Our study used a convergent mixed‐methods design. We examined off‐duty police‐perpetrated killings by victim race and gender, comparing absolute rates and rates relative to total police‐perpetrated killings. Correction added on 26 June 2023, after first online publication: ‘policy‐perpetrated’ has been changed to ‘police‐perpetrated’ in the preceding sentence. We also conducted race‐gender comparisons of the frequency of news media reporting of these killings, and whether reporting identified the perpetrator as an off‐duty officer. We conducted thematic analysis of the narrative free‐text field that accompanied quantitative data using grounded theory.
Principal Findings
Black men were the most frequent victims killed by off‐duty police (39.3%) followed by white men (25.2%), Hispanic men (11.2%), white women (9.1%), men of unknown race (9.1%), and Black women (4.1%). Black women had the highest rate of off‐duty/total police‐perpetrated killings relative to white men (rate = 12.82%, RR = 8.32, 95% CI: 4.43–15.63). There were threefold higher odds of news reporting of a police‐perpetrated killing and the off‐duty status of the officer for incidents with Black and Hispanic victims. Qualitative analysis revealed that off‐duty officers intervened violently within their own social networks; their presence escalated situations; they intentionally obscured information about their lethal violence; they intervened while impaired; their victims were often in crisis; and their intervention posed harm and potential secondary traumatization to witnesses.
Conclusions
Police perpetrate lethal violence while off duty, compromising public health and safety. Additionally, off‐duty police‐perpetrated killings are reported differentially by the news media depending on the race of the victim.
As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will ...be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.
What Is Herd Immunity? Desai, Angel N; Majumder, Maimuna S
JAMA,
11/2020, Letnik:
324, Številka:
20
Journal Article, Reference
Recenzirano
Insights are presented into how herd immunity can be achieved in the context of the COVID-19 pandemic. Herd immunity occurs when a significant portion of a population becomes immune to an infectious ...disease, limiting further disease spread.
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