Evaluating the impact of multiple factors on the control of COVID-19 epidemic: A modelling analysis using India as a case study.

The currently ongoing COVID-19 outbreak remains a global health concern. Understanding the transmission modes of COVID-19 can help develop more effective prevention and control strategies. In this study, we devise a two-strain nonlinear dynamical model with the purpose to shed light on the effect of multiple factors on the outbreak of the epidemic. Our targeted model incorporates the simultaneous transmission of the mutant strain and wild strain, environmental transmission and the implementation of vaccination, in the context of shortage of essential medical resources. By using the nonlinear least-square method, the model is validated based on the daily case data of the second COVID-19 wave in India, which has triggered a heavy load of confirmed cases. We present the formula for the effective reproduction number and give an estimate of it over the time. By conducting Latin Hyperbolic Sampling (LHS), evaluating the partial rank correlation coefficients (PRCCs) and other sensitivity analysis, we have found that increasing the transmission probability in contact with the mutant strain, the proportion of infecteds with mutant strain, the ratio of probability of the vaccinated individuals being infected, or the indirect transmission rate, all could aggravate the outbreak by raising the total number of deaths. We also found that increasing the recovery rate of those infecteds with mutant strain while decreasing their disease-induced death rate, or raising the vaccination rate, both could alleviate the outbreak by reducing the deaths. Our results demonstrate that reducing the prevalence of the mutant strain, improving the clearance of the virus in the environment, and strengthening the ability to treat infected individuals are critical to mitigate and control the spread of COVID-19, especially in the resource-constrained regions.

Addressing COVID-19 Vaccine Hesitancy: The Role of Medical Students.

We conducted a survey study at the Icahn School of Medicine at Mount Sinai to assess COVID-19 vaccine attitudes and behaviors among medical students. Almost all respondents (96.5%, n = 222) believed vaccines were effective and reported being asked about the COVID-19 vaccine by family members (79.0%, n = 180). However, when asked how they respond when someone shares misinformation, 89.0% (n = 202) responded they agree to avoid conflict, 44.9% (n = 102) listened empathically, and 9.3% (n = 21) corrected the misinformation. Medical school education can address this disconnect, using standardized patients and role-playing to give students the tools to address vaccine hesitancy within their communities. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01670-2.

Texas Public Agencies' Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach.

BACKGROUND: The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. OBJECTIVE: This study examines the content of COVID-19-related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. METHODS: All COVID-19-related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet's functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. RESULTS: The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. CONCLUSIONS: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences' self-efficacy.

Addressing COVID-19 Vaccine Hesitancy: The Role of Medical Students

We conducted a survey study at the Icahn School of Medicine at Mount Sinai to assess COVID-19 vaccine attitudes and behaviors among medical students. Almost all respondents (96.5%, n = 222) believed vaccines were effective and reported being asked about the COVID-19 vaccine by family members (79.0%, n = 180). However, when asked how they respond when someone shares misinformation, 89.0% (n = 202) responded they agree to avoid conflict, 44.9% (n = 102) listened empathically, and 9.3% (n = 21) corrected the misinformation. Medical school education can address this disconnect, using standardized patients and role-playing to give students the tools to address vaccine hesitancy within their communities. Supplementary Information The online version contains supplementary material available at 10.1007/s40670-022-01670-2.

Is replacing red meat with other protein sources associated with lower risks of coronary heart disease and all-cause mortality? A meta-analysis of prospective studies.

CONTEXT: A high amount of red meat consumption has been associated with higher risks of coronary heart disease (CHD) and all-cause mortality in a single food-exposure model. However, this model may overlook the potentially differential influence of red meat on these outcomes depending on the foods replaced by red meat. OBJECTIVE: A PRISMA-compliant meta-analysis of prospective observational studies was performed to quantify the risks of CHD and all-cause mortality associated with the replacement of total, unprocessed, or processed red meat with fish/seafood, poultry, dairy, eggs, nuts, and legumes. DATA SOURCES: The PubMed and Web of Science databases were searched to identify relevant articles published in any language from database inception to October 30, 2021. DATA EXTRACTION: The prospective observational studies were considered relevant if they reported relative risks (RRs) and 95%CIs for the associations of interest. DATA ANALYSIS: Thirteen articles were included. A random-effects model was used to estimate the summary RRs and 95%CIs for the associations of interest. Replacing total red meat with poultry (RR, 0.88, 95%CI, 0.82-0.96; I2 = 0%), dairy (RR, 0.90, 95%CI, 0.88-0.92; I2 = 0%), eggs (RR, 0.86, 95%CI, 0.79-0.94; I2 = 7.1%), nuts (RR, 0.84, 95%CI, 0.74-0.95; I2 = 66.8%), or legumes (RR, 0.84, 95%CI, 0.74-0.95; I2 = 7.3%) was associated with a lower risk of CHD, whereas substituting fish/seafood (RR, 0.91, 95%CI, 0.79-1.04; I2 = 69.5%) for total red meat was not associated with the risk of CHD. The replacement of total red meat with fish/seafood (RR, 0.92, 95%CI, 0.89-0.96; I2 = 86.9%), poultry (RR, 0.92, 95%CI, 0.90-0.95; I2 = 61.6%), eggs (RR, 0.91, 95%CI, 0.87-0.95; I2 = 33.8%), or nuts (RR, 0.92, 95%CI, 0.87-0.97; I2 = 81.9%) was associated with a lower risk of all-cause mortality, whereas the substitution of dairy (RR, 0.97, 95%CI, 0.93-1.01; I2 = 33.9%) or legumes (RR, 0.97, 95%CI, 0.93-1.01; I2 = 53.5%) for total red meat was not associated with the risk of all-cause mortality. Lower risks of CHD and all-cause mortality were more consistently observed for processed red meat replacements than for unprocessed red meat replacements. The results did not materially change when the analyses of total, processed, and unprocessed red meat were restricted to the studies that used a uniform substitution amount per unit of 1 serving/d. CONCLUSION: Keeping red meat, particularly processed red meat, consumption to a minimum along with increasing healthier alternative protein sources to replace red meat in the diet may contribute to the prevention of CHD and premature death. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021259446.

Modeling the effect of Fangcang shelter hospitals on the control of COVID-19 epidemic.

The ongoing COVID-19 pandemic has posed a tremendous threat to the public and health authorities. Wuhan, as one of the cities experiencing the earliest COVID-19 outbreak, has successfully tackled the epidemic finally. The main reason is the implementing of Fangcang shelter hospitals, which rapidly and massively scale the health system's capacity to treat COVID-19 confirmed cases with mild symptoms. To give insights on what degree Fangcang shelter hospitals have contained COVID-19 in Wuhan, we proposed a piecewise smooth model regarding the patient triage scheme and the bed capacities of Fangcang shelter hospitals and designated hospitals. We used data on the cumulative number of confirmed cases, recovered cases, deaths, and data on the number of hospitalized individuals in Fangcang shelter hospitals and designated hospitals in Wuhan to parameterize the targeted model. Our results showed that diminishing the bed capacity or delaying the opening time of Fangcang shelter hospitals, both would result in worsening the epidemic by increasing the total number of infectives and hospitalized individuals and the effective reproduction number R e ( t ) . The findings demonstrated that Fangcang shelter hospitals avoided 17,013 critical infections and 17,823 total infections while it saved 7 days during the process of controlling the effective reproduction number R e ( t ) < 1 . Our study highlighted the critical role of Fangcang shelter hospitals in curbing and eventually stopping COVID-19 outbreak in Wuhan, China. These findings may provide a valuable reference for decision-makers in regarding ramping up the health system capacity to isolate groups of people with mild symptoms in areas of widespread infection.

Neighborhood characteristics associated with COVID-19 burden-the modifying effect of age.

BACKGROUND: Neighborhood characteristics have been linked to community incidence of COVID-19, but the modifying effect of age has not been examined. OBJECTIVE: We adapted a neighborhood-wide analysis study (NWAS) design to systematically examine associations between neighborhood characteristics and COVID-19 incidence among different age groups. METHODS: The number of daily cumulative cases of COVID-19 by zip code area in Illinois has been made publicly available by the Illinois Department of Public Health. The number of COVID-19 cases was reported for eight age groups (under 20, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+). We reviewed this data published from May 23 through June 17, 2020 with complete data for all eight age groups and linked the data to neighborhood characteristics measured by the American Community Survey (ACS). Geographic age-specific cumulative incidence (cases per 1000 people) of COVID-19 was calculated by dividing the number of daily cumulative cases by the population of the same age group at each zip code area. The association between individual characteristics and COVID-19 incidence was examined using Poisson regression models. RESULTS: At the zip code level, neighborhood socioeconomic status was a more important risk factor of COVID-19 incidence in children and working-age adults than in seniors. Social demographics and housing conditions were important risk factors of COVID-19 incidence in older age groups. We additionally observed significant associations between transportation-related variables and COVID-19 incidences in multiple age groups. SIGNIFICANCE: We concluded that age modified the association between neighborhood characteristics and COVID-19 incidence.