Effect of high and low risk susceptibles in the transmission dynamics of COVID-19 and control strategies.

In this study, we formulate and analyze a deterministic model for the transmission of COVID-19 and evaluate control strategies for the epidemic. It has been well documented that the severity of the disease and disease related mortality is strongly correlated with age and the presence of co-morbidities. We incorporate this in our model by considering two susceptible classes, a high risk, and a low risk group. Disease transmission within each group is modelled by an extension of the SEIR model, considering additional compartments for quarantined and treated population groups first and vaccinated and treated population groups next. Cross Infection across the high and low risk groups is also incorporated in the model. We calculate the basic reproduction number [Formula: see text] and show that for [Formula: see text] the disease dies out, and for [Formula: see text] the disease is endemic. We note that varying the relative proportion of high and low risk susceptibles has a strong effect on the disease burden and mortality. We devise optimal medication and vaccination strategies for effective control of the disease. Our analysis shows that vaccinating and medicating both groups is needed for effective disease control and the controls are not very sensitive to the proportion of the high and low risk populations.

SARS-CoV-2 in animals: From potential hosts to animal models.

Within only one year after the first detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), nearly 100 million infections were reported in the human population globally, with more than two million fatal cases. While SARS-CoV-2 most likely originated from a natural wildlife reservoir, neither the immediate viral precursor nor the reservoir or intermediate hosts have been identified conclusively. Due to its zoonotic origin, SARS-CoV-2 may also be relevant to animals. Thus, to evaluate the host range of the virus and to assess the risk to act as potential animal reservoir, a large number of different animal species were experimentally infected with SARS-CoV-2 or monitored in the field in the last months. In this review, we provide an update on studies describing permissive and resistant animal species. Using a scoring system based on viral genome detection subsequent to SARS-CoV-2 inoculation, seroconversion, the development of clinical signs and transmission to conspecifics or humans, the susceptibility of diverse animal species was classified on a semi-quantitative scale. While major livestock species such as pigs, cattle and poultry are mostly resistant, companion animals appear moderately susceptible, while several model animal species used in research, including several Cricetidae species and non-human primates, are highly susceptible to SARS-CoV-2 infection. By natural infections, it became obvious that American minks (Neovison vison) in fur farms, e.g., in the Netherlands and Denmark are highly susceptible resulting in local epidemics in these animals.

Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection.

Importance: Understanding the effect of serum antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on susceptibility to infection is important for identifying at-risk populations and could have implications for vaccine deployment. Objective: The study purpose was to evaluate evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test (NAAT) among patients with positive vs negative test results for antibodies in an observational descriptive cohort study of clinical laboratory and linked claims data. Design, Setting, and Participants: The study created cohorts from a deidentified data set composed of commercial laboratory tests, medical and pharmacy claims, electronic health records, and hospital chargemaster data. Patients were categorized as antibody-positive or antibody-negative according to their first SARS-CoV-2 antibody test in the database. Main Outcomes and Measures: Primary end points were post-index diagnostic NAAT results, with infection defined as a positive diagnostic test post-index, measured in 30-day intervals (0-30, 31-60, 61-90, >90 days). Additional measures included demographic, geographic, and clinical characteristics at the time of the index antibody test, including recorded signs and symptoms or prior evidence of coronavirus 2019 (COVID) diagnoses or positive NAAT results and recorded comorbidities. Results: The cohort included 3 257 478 unique patients with an index antibody test; 56% were female with a median (SD) age of 48 (20) years. Of these, 2 876 773 (88.3%) had a negative index antibody result, and 378 606 (11.6%) had a positive index antibody result. Patients with a negative antibody test result were older than those with a positive result (mean age 48 vs 44 years). Of index-positive patients, 18.4% converted to seronegative over the follow-up period. During the follow-up periods, the ratio (95% CI) of positive NAAT results among individuals who had a positive antibody test at index vs those with a negative antibody test at index was 2.85 (95% CI, 2.73-2.97) at 0 to 30 days, 0.67 (95% CI, 0.6-0.74) at 31 to 60 days, 0.29 (95% CI, 0.24-0.35) at 61 to 90 days, and 0.10 (95% CI, 0.05-0.19) at more than 90 days. Conclusions and Relevance: In this cohort study, patients with positive antibody test results were initially more likely to have positive NAAT results, consistent with prolonged RNA shedding, but became markedly less likely to have positive NAAT results over time, suggesting that seropositivity is associated with protection from infection. The duration of protection is unknown, and protection may wane over time.

Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21st, 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21st until May 4th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. METHODS: This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. RESULTS: The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. CONCLUSIONS: Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

COVID-19 Susceptibility Mapping Using Multicriteria Evaluation.

Objective: The purpose of this research was to investigate coronavirus disease (COVID-19) susceptibility in districts of Bangladesh using multicriteria evaluation techniques.Methods: Secondary data were collected from different government organizations, 120 primary surveys were conducted for calculating weights, and results were validated through 12 key people's interviews. Pairwise comparison matrixes were calculated for 9 factors and subfactors. The analytic hierarchy process used for calculating the susceptibility index and map was prepared based on the results.Results: According to the results, multiple causal factors might be responsible for COVID-19 spreading in Bangladesh. Dhaka might be vulnerable to COVID-19 due to a higher population, population density, and international collaboration. According to the pairwise comparison matrix, the consistency ratio for subfactors and factors was in the permissible limit (ie, less than 0.10). The highest factor weight of 0.2907 was found for the factors type of port. The maximum value for the susceptibility index was 0.435219362 for Chittagong, and the minimum value was 0.076174 for Naogaon.Conclusions: The findings of this research might help the communities and government agencies with effective decision-making.

Risk estimation of the SARS-CoV-2 acute respiratory disease outbreak outside China.

BACKGROUND: On December 31, 2019, the World Health Organization was alerted to the occurrence of cases of pneumonia in Wuhan, Hubei Province, China, that were caused by an unknown virus, which was later identified as a coronavirus and named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate the reproductive number of SARS-CoV-2 in the Hubei Province and evaluate the risk of an acute respiratory coronavirus disease (COVID-19) outbreak outside China by using a mathematical model and stochastic simulations. RESULTS: We constructed a mathematical model of SARS-CoV-2 transmission dynamics, estimated the rate of transmission, and calculated the reproductive number in Hubei Province by using case-report data from January 11 to February 6, 2020. The possible number of secondary cases outside China was estimated by stochastic simulations in various scenarios of reductions in the duration to quarantine and rate of transmission. The rate of transmission was estimated as 0.8238 (95% confidence interval [CI] 0.8095-0.8382), and the basic reproductive number as 4.1192 (95% CI 4.0473-4.1912). Assuming the same rate of transmission as in Hubei Province, the possibility of no local transmission is 54.9% with a 24-h quarantine strategy, and the possibility of more than 20 local transmission cases is 7% outside of China. CONCLUSION: The reproductive number for SARS-CoV-2 transmission dynamics is significantly higher compared to that of the previous SARS epidemic in China. This implies that human-to-human transmission is a significant factor for contagion in Hubei Province. Results of the stochastic simulation emphasize the role of quarantine implementation, which is critical to prevent and control the SARS-CoV-2 outbreak outside China.