COVID-19 vaccine hesitancy and attitudes in Pakistan: a cross-sectional phone survey of major urban cities.

BACKGROUND: COVID-19 mass vaccination is the only hopeful savior to curb the pandemic. Vaccine distribution to achieve herd immunity is hindered by hesitance and negative attitude of the public against COVID-19 vaccination. This study aims to evaluate the vaccine hesitancy and attitudes in major cities in Pakistan as well as their determinants. METHODS: A cross-sectional telephonic survey was conducted in June 2021 in major cities of Pakistan including Karachi, Lahore, Islamabad, Peshawar, and Gilgit, from unvaccinated urban population aged 18 years or older. Random Digit Dialing through multi-stage stratified random sampling was used to ensure representation of each target city and socio-economic classes. Questionnaire collected information on socio-demographics, COVID-19-related experiences, risk perception of infection, and receptivity of COVID-19 vaccination. Multivariate logistic regression analyses were performed to identify key determinants of vaccine hesitancy and acceptance. RESULTS: The prevalence of vaccinated population in this survey was 15%. Of the 2270 respondents, 65% respondents were willing to vaccinate, while only 19% were registered for vaccination. Factors significantly associated with vaccine willingness were older age (aOR: 6.48, 95% CI: 1.94-21.58), tertiary education (aOR: 2.02, 95% CI: 1.36, 3.01), being employed (aOR: 1.34, 95% CI: 1.01, 1.78), perceived risk of COVID-19 (aOR: 4.38, 95% CI: 2.70, 7.12), and higher compliance with standard operating procedures (aOR: 1.72, 95% CI: 1.26, 2.35). The most common vaccine hesitancy reasons were 'no need' (n = 284, 36%) and concerns with 'vaccine safety and side effects' (n = 251, 31%), while most reported vaccine motivation reasons were 'health safety' (n = 1029, 70%) and 'to end the pandemic' (n = 357, 24%). CONCLUSIONS: Although our study found 35% hesitancy rate of COVID-19 vaccine, there were noticeable demographic differences that suggest tailored communication strategy to address concerns held by most hesitant subpopulation. Use of mobile vaccination facilities particularly for less mobile and disadvantaged, and implementation and evaluation of social mobilization strategy should be considered to increase overall COVID-19 vaccination acceptance and coverage.

School closures help reduce the spread of COVID-19: A pre- and post-intervention analysis in Pakistan

Closing schools to control COVID-19 transmission has been globally debated, with concerns about children's education and well-being, and also because of the varied effectiveness of the intervention in studies across the world. This paper aims to determine the effect of school closure policy on the incidence of COVID-19 in Pakistan. A Difference-in-Differences (DiD) analysis compared changes in COVID-19 incidence across cities that completely (Islamabad) and partially (Peshawar) closed schools during the second wave of COVID-19 in Pakistan. Effects of closing (November 2020) and reopening schools (February 2021) were assessed in Islamabad and Peshawar 10 and 20 days after policy implementation. In Islamabad, there was a greater decline in cases than in Peshawar when schools closed. After 10-days, the average reduction of daily COVID-19 incidence in Islamabad was lower by 89 cases (95% CI: -196, 18), due to complete school closure, with a relative reduction of 125 cases (95% CI: -191, -59) compared to Peshawar. Similarly, the relative increase in Islamabad after schools re-opened was 107 cases (95% CI: 46, 167) compared to Peshawar. After 20-days, the average daily COVID-19 incidence in both cities declined after school were closed (Islamabad: -81 [95% CI: -150, -13] versus Peshawar: -80 [95% CI: -148, -12]). COVID-19 incidence appeared to decline after schools reopened as well (Islamabad: -116 [95% CI: -230, -3] versus Peshawar: -30 [95% CI: -124, 63]). However, Peshawar's decline is not statistically significant. These results control for changes in testing as well as a daily time trend. The magnitude and speed of reduction in cases with a complete school closure, and a similar but reverse trend of increasing cases upon reopening, suggests that closing schools reduces COVID-19 transmission in communities. However, there are learning-loss and well-being costs for children and their parents.

Impact of school closures and reopening on COVID-19 caseload in 6 cities of Pakistan: An Interrupted Time Series Analysis

Schools were closed all over Pakistan on November 26, 2020 to reduce community transmission of COVID-19 and reopened between January 18 and February 1, 2021. However, these closures were associated with significant economic and social costs, prompting a review of effectiveness of school closures to reduce the spread of COVID-19 infections in a developing country like Pakistan. A single-group interrupted time series analysis (ITSA) was used to measure the impact of school closures, as well as reopening schools, on daily new COVID-19 cases in 6 major cities across Pakistan: Lahore, Karachi, Islamabad, Quetta, Peshawar, and Muzaffarabad. However, any benefits were contingent on continued closure of schools, as cases bounced back once schools reopened. School closures are associated with a clear and statistically significant reduction in COVID-19 cases by 0.07 to 0.63 cases per 100,000 population, while reopening schools is associated with a statistically significant increase. Lahore is an exception to the effect of school closures, but it too saw an increase in COVID-19 cases after schools reopened in early 2021. We show that closing schools was a viable policy option, especially before vaccines became available. However, its social and economic costs must also be considered.

Community engagement to increase vaccine uptake: Quasi-experimental evidence from Islamabad and Rawalpindi, Pakistan.

Developing countries have been facing difficulties in reaching out to low-income and underserved communities for COVID-19 vaccination coverage. The rapidity of vaccine development caused a mistrust among certain subgroups of the population, and hence innovative approaches were taken to reach out to such populations. Using a sample of 1760 respondents in five low-income, informal localities of Islamabad and Rawalpindi, Pakistan, we evaluated a set of interventions involving community engagement by addressing demand and access barriers. We used multi-level mixed effects models to estimate average treatment effects across treatment areas. We found that our interventions increased COVID-19 vaccine willingness in two treatment areas that are furthest from city centers by 7.6% and 6.6% respectively, while vaccine uptake increased in one of the treatment areas by 17.1%, compared to the control area. Our results suggest that personalized information campaigns such as community mobilization help to increase COVID-19 vaccine willingness. Increasing uptake however, requires improving access to the vaccination services. Both information and access may be different for various communities and therefore a "one-size-fits-all" approach may need to be better localized. Such underserved and marginalized communities are better served if vaccination efforts are contextualized.

Connecting SiO4 in Silicate and Silicate Chain Networks to Compute Kulli Temperature Indices.

A topological index is a numerical parameter that is derived mathematically from a graph structure. In chemical graph theory, these indices are used to quantify the chemical properties of chemical compounds. We compute the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index and the F temperature index of a molecular graph silicate network and silicate chain network. Furthermore, a QSPR study of the key topological indices is provided, and it is demonstrated that these topological indices are substantially linked with the physicochemical features of COVID-19 medicines. This theoretical method to find the temperature indices may help chemists and others in the pharmaceutical industry forecast the properties of silicate networks and silicate chain networks before trying.

School closures help reduce the spread of COVID-19: A pre- and post-intervention analysis in Pakistan.

Closing schools to control COVID-19 transmission has been globally debated, with concerns about children's education and well-being, and also because of the varied effectiveness of the intervention in studies across the world. This paper aims to determine the effect of school closure policy on the incidence of COVID-19 in Pakistan. A Difference-in-Differences (DiD) analysis compared changes in COVID-19 incidence across cities that completely (Islamabad) and partially (Peshawar) closed schools during the second wave of COVID-19 in Pakistan. Effects of closing (November 2020) and reopening schools (February 2021) were assessed in Islamabad and Peshawar 10 and 20 days after policy implementation. In Islamabad, there was a greater decline in cases than in Peshawar when schools closed. After 10-days, the average reduction of daily COVID-19 incidence in Islamabad was lower by 89 cases (95% CI: -196, 18), due to complete school closure, with a relative reduction of 125 cases (95% CI: -191, -59) compared to Peshawar. Similarly, the relative increase in Islamabad after schools re-opened was 107 cases (95% CI: 46, 167) compared to Peshawar. After 20-days, the average daily COVID-19 incidence in both cities declined after school were closed (Islamabad: -81 [95% CI: -150, -13] versus Peshawar: -80 [95% CI: -148, -12]). COVID-19 incidence appeared to decline after schools reopened as well (Islamabad: -116 [95% CI: -230, -3] versus Peshawar: -30 [95% CI: -124, 63]). However, Peshawar's decline is not statistically significant. These results control for changes in testing as well as a daily time trend. The magnitude and speed of reduction in cases with a complete school closure, and a similar but reverse trend of increasing cases upon reopening, suggests that closing schools reduces COVID-19 transmission in communities. However, there are learning-loss and well-being costs for children and their parents.

Optimal control problem arising from COVID-19 transmission model with rapid-test.

The world health organization (WHO) has declared the Coronavirus (COVID-19) a pandemic. In light of this ongoing global issue, different health and safety measure has been recommended by the WHO to ensure the proactive, comprehensive, and coordinated steps to bring back the whole world into a normal situation. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates which consider the rapid-test as the intervention program. Therefore, we have developed the biologically feasible region, i.e., positively invariant for the model and boundedness solution of the system. Our system becomes well-posed mathematically and epidemiologically for sensitive analysis and our analytical result shows an occurrence of a forward bifurcation when the basic reproduction number is equal to unity. Further, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half-normalizations, and full normalizations. The numerical approximations have been measured by using System Biology Toolbox (SBedit) with MATLAB, and the model is analyzed graphically. Our result on the sensitivity analysis shows a potential of rapid-test for the eradication program of COVID-19. Therefore, we continue our result by reconstructing our model as an optimal control problem. Our numerical simulation shows a time-dependent rapid test intervention succeeded in suppressing the spread of COVID-19 effectively with a low cost of the intervention. Finally, we forecast three COVID-19 incidence data from China, Italy, and Pakistan. Our result suggests that Italy already shows a decreasing trend of cases, while Pakistan is getting closer to the peak of COVID-19.

Balancing science and public policy in Pakistan's COVID-19 response.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has affected the world in an unprecedented manner and South Asian countries were among the first to experience imported cases. Pakistan's response to COVID-19 has been under scrutiny for its granularity, reach and impact. AIMS: to evaluate objectively the chronology and depth of the response to COVID-19 in Pakistan. METHODS: We evaluated available national and subnational epidemiological and burden information on COVID-19 cases and deaths in Pakistan, including projection models available to the Government at an early stage of the pandemic. RESULTS: Pakistan, with a population of 215 million and considerable geographic diversity, experienced case introduction from pilgrims returning from the Islamic Republic of Iran, followed by widespread community transmission. The National Command and Operations Centre, established through civilian and military partnership, was critical in fast tracking logistics, information gathering, real-time reporting and smart lockdowns, coupled with a massive cash support programme targeting the poorest sections of society. Cases peaked in June 2020 but the health system was able to cope with the excess workload. Since then, although testing rates remain low (> 300 000 cases confirmed to date), case fatality rates have stabilized, and with 6300 deaths, Pakistan seems to have flattened the COVID-19 curve. CONCLUSION: Despite notable successes in controlling the pandemic, several weaknesses remain and there are risks of rebound as the economy and educational systems reopen. There is continued need for strong technical and programmatic oversight, linked to civic society engagement and working with religious scholars to ensure nonpharmacological intervention compliance.

Infection control practices and challenges in Pakistan during the COVID-19 pandemic: a multicentre cross-sectional study.

BACKGROUND: Patient and staff safety at healthcare facilities during outbreaks hinges on a prompt infection prevention and control response. Physicians leading these programmes have encountered numerous obstacles during the pandemic. AIM/OBJECTIVE: The aim of this study was to evaluate infection prevention and control practices and explore the challenges in Pakistan during the coronavirus disease 2019 pandemic. METHODS: We conducted a cross-sectional study and administered a survey to physicians leading infection prevention and control programmes at 18 hospitals in Pakistan. RESULTS: All participants implemented universal masking, limited the intake of patients and designated separate triage areas, wards and intensive care units for coronavirus disease 2019 patients at their hospitals. Eleven (61%) physicians reported personal protective equipment shortages. Staff at three (17%) hospitals worked without the appropriate personal protective equipment due to limited supplies. All participants felt overworked and 17 (94%) reported stress. Physicians identified the lack of negative pressure rooms, fear and anxiety among hospital staff, rapidly evolving guidelines, personal protective equipment shortages and opposition from hospital staff regarding the choice of recommended personal protective equipment as major challenges during the pandemic. DISCUSSION: The results of this study highlight the challenges faced by physicians leading infection prevention and control programmes in Pakistan. It is essential to support infection prevention and control personnel and bridge the identified gaps to ensure patient and staff safety at healthcare facilities.

Dynamics models for identifying the key transmission parameters of the COVID-19 disease

After the analysis and forecast of COVID-19 spreading in China, Italy, and France the WHO has declared the COVID-19 a pandemic. There are around 100 research groups across the world trying to develop a vaccine for this coronavirus. Therefore, the quantitative and qualitative analysis of the COVID–19 pandemic is needed along with the effect of rapid test infection identification on controlling the spread of COVID-19. Mathematical models with computational simulations are the effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. In this paper, we develop the models for coronavirus disease at different stages with the addition of more parameters due to interactions among the individuals. Then, some key computational simulations and sensitivity analysis are investigated. Further, the local sensitivities for each model state concerning the model parameters are computed using the model reduction techniques: the dynamical models are eventually changed with the change of parameters are represented graphically.

A quantitative and qualitative analysis of the COVID-19 pandemic model.

Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.