Extinction and persistence of a stochastic delayed Covid-19 epidemic model.

We formulated a Coronavirus (COVID-19) delay epidemic model with random perturbations, consisting of three different classes, namely the susceptible population, the infectious population, and the quarantine population. We studied the proposed problem to derive at least one unique solution in the positive feasweible region of the non-local solution. Sufficient conditions for the extinction and persistence of the proposed model are established. Our results show that the influence of Brownian motion and noise on the transmission of the epidemic is very large. We use the first-order stochastic Milstein scheme, taking into account the required delay of infected individuals.

Extinction and stationary distribution of a stochastic COVID-19 epidemic model with time-delay.

We reformulate a stochastic epidemic model consisting of four human classes. We show that there exists a unique positive solution to the proposed model. The stochastic basic reproduction number R0s is established. A stationary distribution (SD) under several conditions is obtained by incorporating stochastic Lyapunov function. The extinction for the proposed disease model is obtained by using the local martingale theorem. The first order stochastic Runge-Kutta method is taken into account to depict the numerical simulations.

Fractional dynamics and stability analysis of COVID-19 pandemic model under the harmonic mean type incidence rate.

In this research, COVID-19 model is formulated by incorporating harmonic mean type incidence rate which is more realistic in average speed. Basic reproduction number, equilibrium points, and stability of the proposed model is established under certain conditions. Runge-Kutta fourth order approximation is used to solve the deterministic model. The model is then fractionalized by using Caputo-Fabrizio derivative and the existence and uniqueness of the solution are proved by using Banach and Leray-Schauder alternative type theorems. For the fractional numerical simulations, we use the Adam-Moulton scheme. Sensitivity analysis of the proposed deterministic model is studied to identify those parameters which are highly influential on basic reproduction number.

Fractional optimal control of COVID-19 pandemic model with generalized Mittag-Leffler function.

In this paper, we consider a fractional COVID-19 epidemic model with a convex incidence rate. The Atangana-Baleanu fractional operator in the Caputo sense is taken into account. We establish the equilibrium points, basic reproduction number, and local stability at both the equilibrium points. The existence and uniqueness of the solution are proved by using Banach and Leray-Schauder alternative type theorems. For the fractional numerical simulations, we use the Toufik-Atangana scheme. Optimal control analysis is carried out to minimize the infection and maximize the susceptible people.

Seroprevalence and Risk Factors of SARS CoV-2 in Health Care Workers of Tertiary-Care Hospitals in the Province of Khyber Pakhtunkhwa, Pakistan (preprint)

Background: High number of SARS CoV2 infected patients has overburdened healthcare delivery system, particularly in low-income countries. In the recent past many studies from the developed countries have been published on the prevalence of SARS CoV2 antibodies and the risk factors of COVID 19 in healthcare-workers but little is known from developing countries. Methods: This cross-sectional study was conducted on prevalence of SARS CoV2 antibody and risk factors for seropositivity in HCWs in tertiary care hospitals of Peshawar city, Khyber Pakhtunkhwa province Pakistan. Findings: The overall seroprevalence of SARS CoV2 antibodies was 30.7% (CI, 27.8 to 33.6) in 1011 HCWs. Laboratory technicians had the highest seropositivity (50.0%, CI, 31.8 to 68.1). Risk analysis revealed that wearing face-mask and observing social-distancing within a family could reduce the risk (OR:0.67. p<0.05) and (OR:0.73. p<0.05) while the odds of seropositivity were higher among those attending funeral and visiting local-markets (OR:1.83. p<0.05) and (OR:1.66. p<0.01). In Univariable analysis, being a nursing staff and a paramedical staff led to higher risk of seropositivity (OR:1.58. p< 0.05), (OR:1.79. p< 0.05). Fever (OR:2.36, CI, 1.52 to 3.68) and loss of smell (OR:2.95, CI: 1.46 to 5.98) were significantly associated with increased risk of seropositivity (p<0.01). Among the seropositive HCWs, 165 (53.2%) had no symptoms at all while 145 (46.8%) had one or more symptoms. Interpretation: The high prevalence of SARS CoV2 antibodies in HCWs warrants for better training and use of protective measure to reduce their risk. Early detection of asymptomatic HCWs may be of special importance because they are likely to be potential threat to others during the active phase of viremia. Funding: Prime Foundation Pakistan.

COVID19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis? (preprint)

The COVID-19 is a highly contagious viral infection which played havoc on everyone's life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

Genetic analysis of SARS-CoV-2 isolates collected from Bangladesh: insights into the origin, mutation spectrum, and possible pathomechanism (preprint)

As the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), rages across the world, killing hundreds of thousands and infecting millions, researchers are racing against time to elucidate the viral genome. Some Bangladeshi institutes are also in this race, sequenced a few isolates of the virus collected from Bangladesh. Here, we present a genomic analysis of 14 isolates. The analysis revealed that SARS-CoV-2 isolates sequenced from Dhaka and Chittagong were the lineage of Europe and the Middle East, respectively. Our analysis identified a total of 42 mutations, including three large deletions, half of which were synonymous. Most of the missense mutations in Bangladeshi isolates found to have weak effects on the pathogenesis. Some mutations may lead the virus to be less pathogenic than the other countries. Molecular docking analysis to evaluate the effect of the mutations on the interaction between the viral spike proteins and the human ACE2 receptor, though no significant interaction was observed. This study provides some preliminary insights into the origin of Bangladeshi SARS-CoV-2 isolates, mutation spectrum and its possible pathomechanism, which may give an essential clue for designing therapeutics and management of COVID-19 in Bangladesh.