Commentary on Ferguson, et al., "Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand"

Utilizing disease transmission and response capacities to optimize covid-19 control in Malaysia

OBJECTIVES

Public Health Social Measures (PHSM) such as movement restriction movement needed to be adjusted accordingly during the COVID-19 pandemic to ensure low disease transmission alongside adequate health system capacities based on the COVID-19 situational matrix proposed by the World Health Organization (WHO). This paper aims to develop a mechanism to determine the COVID-19 situational matrix to adjust movement restriction intensity for the control of COVID-19 in Malaysia.

METHODS

Several epidemiological indicators were selected based on the WHO PHSM interim guidance report and validated individually and in several combinations to estimate the community transmission level (CT) and health system response capacity (RC) variables. Correlation analysis between CT and RC with COVID-19 cases was performed to determine the most appropriate CT and RC variables. Subsequently, the CT and RC variables were combined to form a composite COVID-19 situational matrix (SL). The SL matrix was validated using correlation analysis with COVID-19 case trends. Subsequently, an automated web-based system that generated daily CT, RC, and SL was developed.

RESULTS

CT and RC variables were estimated using case incidence and hospitalization rate; Hospital bed capacity and COVID-19 ICU occupancy respectively. The estimated CT and RC were strongly correlated [ρ = 0.806 (95% CI 0.752, 0.848); and ρ = 0.814 (95% CI 0.778, 0.839), p < 0.001] with the COVID-19 cases. The estimated SL was strongly correlated with COVID-19 cases (ρ = 0.845, p < 0.001) and responded well to the various COVID-19 case trends during the pandemic. SL changes occurred earlier during the increase of cases but slower during the decrease, indicating a conservative response. The automated web-based system developed produced daily real-time CT, RC, and SL for the COVID-19 pandemic.

CONCLUSIONS

The indicators selected and combinations formed were able to generate validated daily CT and RC levels for Malaysia. Subsequently, the CT and RC levels were able to provide accurate and sensitive information for the estimation of SL which provided valuable evidence on the progression of the pandemic and movement restriction adjustment for the control of Malaysia.

Use of bio-information and communication technology shortens time to peak at a lower height of the epidemic curve: An alternative to flattening for countries with early COVID-19 outbreaks

INTRODUCTION

The traditional approach to epidemic control has been to slow down the rate of infection while building up healthcare capacity, resulting in a flattened epidemic curve. Advancements in bio-information-communication technology (BICT) have enabled the preemptive isolation of infected cases through efficient testing and contact tracing. This study aimed to conceptualize the BICT-enabled epidemic control (BICTEC) and to document its relationships with epidemic curve shaping and epidemic mitigation performance.

METHODS

Daily COVID-19 incidences were collected from outbreak to Aug. 12, 2020, for nine countries reporting the first outbreak on or before Feb. 1, 2020. Key epidemic curve determinants-peak height (PH), time to peak (TTP), and area under the curve (AUC)-were estimated for each country, and their relationships were analyzed to test if epidemic curves peak quickly at a shorter height. CFR (Case Fatality Rate) and CI (Cumulative Incidence) were compared across the countries to identify relationships between epidemic curve shapes and epidemic mitigation performance.

RESULTS

China and South Korea had the quickest TTPs (40.70 and 45.37 days since outbreak, respectively) and the shortest PHs (2.95 and 4.65 cases per day, respectively). Sweden, known for its laissez-faire approach, had the longest TTP (120.36) and the highest PH (279.74). Quicker TTPs were correlated with shorter PHs (ρ = 0·896, p = 0·0026) and lower AUCs (0.790, p = 0.0028), indicating that epidemic curves do not follow a flattened trajectory. During the study period, countries with quicker TTPs tended to have lower CIs (ρ = .855, P = .006) and CFRs (ρ = 0.684, P = .061). For example, South Korea, with the second-quickest TTP, reported the second lowest CI and the lowest CFR.

CONCLUSIONS

Countries that experienced early COVID-19 outbreaks demonstrated the epidemic curves that quickly peak at a shorter height, indicating a departure from the traditional flattened trajectory. South Korea's BICTEC was found to be at least as effective as most lockdowns in reducing CI and CFR.

Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions

When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded.

Comparison of COVID-19 vaccine policies and their effectiveness in Korea, Japan, and Singapore

BACKGROUND

This study aimed to analyze coronavirus disease 2019 (COVID-19)vaccine policies and effectiveness in Korea, Japan, and Singapore, thereby providing empirical experience for vaccination and response to similar public health emergencies.

METHODS

The study systematically summarized the COVID-19 vaccine policies in Korea, Japan, and Singapore through public information from the Our World in Data website and the official websites of the Ministries of Health in these three countries.Total vaccinations, COVID-19 vaccination rates, rates of fully vaccinated, rates of boostervaccinated, and total confifirmed cases were selected for cross-sectional comparison of COVID-19 vaccination in these three countries. Combining the basic characteristics of these three countries, daily cases per million, daily deaths per million, and the effective reproduction rate were calculated to measure the effectiveness of COVID-19 vaccine policies implementation in each of these three countries RESULTS: The countermeasures against the COVID-19 in Korea, Japan, and Singapore, although seemingly different on the surface, have all taken an aggressive approach. There are large similarities in the timing of the start of COVID-19 vaccination, the type of vaccine, how vaccine appointments are made, and whether vaccination are free, and all had high vaccination rates. A systematic comparison of the anti-epidemic practices in the three East Asian countries revealed that all three countries experienced more than one outbreak spike due to the spread of new mutant strains after the start of mass vaccination with COVID-19 vaccination, but that vaccination played a positive role in reducing the number of deaths and stabilizing the effective reproduction rate.

CONCLUSIONS

This study comparatively analyzed the COVID-19 vaccine policies and their effects in South Korea, Japan, and Singapore, and found that there is a common set of logical combinations behind the seemingly different strategies of these three countries. Therefore, in the process of combating COVID-19, countries can learn from the successful experience of combating the epidemic and continue to strengthen the implementation of vaccination programs, as well as adjusting public perceptions to reduce the level of vaccine hesitancy, enhance the motivation for vaccination, and improve the coverage of COVID-19 vaccine based on different cultural factors, which remains the direction for future development.

Travel distance, frequency of return, and the spread of disease

Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: [Formula: see text], the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant [Formula: see text] also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable [Formula: see text]. We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics-based not only on travel distance r but also on frequency of return f.

Emerging roles of telemedicine in dementia treatment and care

Dementia is a neurological disorder that affects memory, thinking, orientation, and other important functions of the brain; telemedicine is a part of the healthcare delivery system involving diagnosis and consultation over telecommunications devices such as mobile phones and computers. In this review, we assessed the impact, accessibility, and possible improvements in telemedicine in dementia treatment. Regarding the use of telemedicine in the treatment, we evaluated its impact on the management of the disease (i.e., diagnosis and follow-up). We also evaluated studies on the current improvements and accessibility of telemedicine in dementia treatment. The review findings showed that it is effective in diagnosing patients, monitoring their progress during treatment, and providing caregiver support. However, studies have revealed a lack of accessibility and improvement in telemedicine among the elderly, particularly in West African countries. Finally, lasting solutions were provided to address the problems in the review permanently.

Genetic determination of regional connectivity in modelling the spread of COVID-19 outbreak for more efficient mitigation strategies

For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies. Additionally, thousands of SARS-CoV-2 genomes have been sequenced with associated records, potentially providing a rich source for such spatiotemporal analysis, an unprecedented amount during a single outbreak. Here, in a case study of seven states, we model the first wave of the outbreak by determining regional connectivity from phylogenetic sequence information (i.e. "genetic connectivity"), in addition to traditional epidemiologic and demographic parameters. Our study shows nearly all of the initial outbreak can be traced to a few lineages, rather than disconnected outbreaks, indicative of a mostly continuous initial viral flow. While the geographic distance from hotspots is initially important in the modeling, genetic connectivity becomes increasingly significant later in the first wave. Moreover, our model predicts that isolated local strategies (e.g. relying on herd immunity) can negatively impact neighboring regions, suggesting more efficient mitigation is possible with unified, cross-border interventions. Finally, our results suggest that a few targeted interventions based on connectivity can have an effect similar to that of an overall lockdown. They also suggest that while successful lockdowns are very effective in mitigating an outbreak, less disciplined lockdowns quickly decrease in effectiveness. Our study provides a framework for combining phylodynamic and computational methods to identify targeted interventions.

Evaluation of changes to work patterns in multidisciplinary cancer team meetings due to the COVID-19 pandemic: A national mixed-method survey study

BACKGROUND

It is not well understood the overall changes that multidisciplinary teams (MDTs) have had to make in response to the COVID-19 pandemic, nor the impact that such changes, in addition to the other challenges faced by MDTs, have had on decision-making, communication, or participation in the context of MDT meetings specifically.

METHODS

This was a mixed method, prospective cross-sectional survey study taking place in the United Kingdom between September 2020 and August 2021.

RESULTS

The participants were 423 MDT members. Qualitative findings revealed hybrid working and possibility of virtual attendance as the change introduced because of COVID-19 that MDTs would like to maintain. However, IT-related issues, slower meetings, longer lists and delays were identified as common with improving of the IT infrastructure necessary going forward. In contrast, virtual meetings and increased attendance/availability of clinicians were highlighted as the positive outcomes resulting from the change. Quantitative findings showed significant improvement from before COVID-19 for MDT meeting organisation and logistics (M = 45, SD = 20) compared to the access (M = 50, SD = 12, t(390) = 5.028, p = 0.001), case discussions (M = 50, SD = 14, t(373) = -5.104, p = 0.001), and patient representation (M = 50, SD = 12, t(382) = -4.537, p = 0.001) at MDT meetings.

DISCUSSION

Our study explored the perception of change since COVID-19 among cancer MDTs using mixed methods. While hybrid working was preferred, challenges exist. Significant improvements in the meeting organisation and logistics were reported. Although we found no significant perceived worsening across the four domains investigated, there was an indication in this direction for the case discussions warranting further 'live' assessments of MDT meetings.

A hybrid compartmental model with a case study of COVID-19 in Great Britain and Israel

Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to make informed decisions. Even though there have been countless attempts to propose diverse models since the rise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.

Combating coronavirus disease (COVID-19) in rural areas of Malawi: Factors affecting the fight

BACKGROUND

Owing to their detachment from urban areas, people living in rural areas of Malawi are on the receiving end of health services and socio-economic benefits. The study therefore explored how Malawians living in these areas are adhering to coronavirus disease 2019 (COVID-19) containment measures and the factors that affect the COVID-19 fight amongst this population.

AIM

The study investigated how the rural population in Malawi adheres to COVID-19 containment measures.

SETTING

The study was conducted in two districts in Northern Malawi.

METHODS

Data were collected from 263 participants. The chi-square (χ2) test was performed to determine the association between demographic variables and COVID-19 prevention practices and factors affecting the COVID-19 fight.

RESULTS

Education was significantly associated with wearing of masks (p = 0.01), use of sanitisers (p 0.01) and practising social distancing (p = 0.07). Monthly income was associated with the use of sanitisers (p 0.01). Women were more exposed to fake news about COVID-19 (p = 0.09); older people were more likely to disregard COVID-19 containment measures for cultural reasons (p = 0.07); and monthly income was associated with a lack of resources for following COVID-19 containment measures (p 0.01).

CONCLUSION

Findings show that factors affecting the COVID-19 fight are influenced by various socio-economic factors which should therefore be taken into account in policy planning aiming at controlling the pandemic.Contribution: The study provides health stakeholders with a policy direction that enhances better coordination, implementation and monitoring of COVID-19 response and recovery activities in rural areas in Malawi. The findings have implications on controlling current and future communicable diseases; the proposed strategies might be employed in fighting similar current and future pandemics.

Standardising policy and technology responses in the immediate aftermath of a pandemic: a comparative and conceptual framework

BACKGROUND

The initial policy response to the COVID-19 pandemic has differed widely across countries. Such variability in government interventions has made it difficult for policymakers and health research systems to compare what has happened and the effectiveness of interventions across nations. Timely information and analysis are crucial to addressing the lag between the pandemic and government responses to implement targeted interventions to alleviate the impact of the pandemic.

METHODS

To examine the effect government interventions and technological responses have on epidemiological and economic outcomes, this policy paper proposes a conceptual framework that provides a qualitative taxonomy of government policy directives implemented in the immediate aftermath of a pandemic announcement and before vaccines are implementable. This framework assigns a gradient indicating the intensity and extent of the policy measures and applies the gradient to four countries that share similar institutional features but different COVID-19 experiences: Italy, New Zealand, the United Kingdom and the United States of America.

RESULTS

Using the categorisation framework allows qualitative information to be presented, and more specifically the gradient can show the dynamic impact of policy interventions on specific outcomes. We have observed that the policy categorisation described here can be used by decision-makers to examine the impacts of major viral outbreaks such as SARS-CoV-2 on health and economic outcomes over time. The framework allows for a visualisation of the frequency and comparison of dominant policies and provides a conceptual tool to assess how dominant interventions (and innovations) affect different sets of health and non-health related outcomes during the response phase to the pandemic.

CONCLUSIONS

Policymakers and health researchers should converge toward an optimal set of policy interventions to minimize the costs of the pandemic (i.e., health and economic), and facilitate coordination across governance levels before effective vaccines are produced. The proposed framework provides a useful tool to direct health research system resources and build a policy benchmark for future viral outbreaks where vaccines are not readily available.

Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan

The Japanese government adopted policies to control human mobility in 2020 to prevent the spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). The present study examined the impact of human mobility on COVID-19 cases at the prefectural level in Japan by devising an indicator to have a relationship between the number of infected people and on human mobility. We calculated origin-destination travel mobility within prefectures in Japan from March 1st to December 31st, 2020, using mobile phone data. A cross-correlation function (CCF) was used to examine the relationship between human mobility and a COVID-19 infection acceleration indicator (IAI), which represents the rate of change in the speed of COVID-19 infection. The CCF of intraprefectural human mobility and the IAI in Tokyo showed a maximum value of 0.440 at lag day 12, and the IAI could be used as an indicator to predict COVID-19 cases. Therefore, the IAI and human mobility during the COVID-19 pandemic were useful for predicting infection status. The number of COVID-19 cases was associated with human mobility at the prefectural level in Japan in 2020. Controlling human mobility could help control infectious diseases in a pandemic, especially prior to starting vaccination.

Decentralized governance may lead to higher infection levels and sub-optimal releases of quarantines amid the COVID-19 pandemic

The outbreak of the novel Coronavirus (COVID-19) has led countries worldwide to administer quarantine policies. However, each country or state independently decides what mobility restrictions to administer within its borders while aiming to maximize its own citizens' welfare. Since individuals travel between countries and states, the policy in one country affects the infection levels in other countries. Therefore, a major question is whether the policies dictated by multiple governments could be efficient. Here we focus on the decision regarding the timing of releasing quarantines, which were common during the first year of the pandemic. We consider a game-theoretical epidemiological model in which each government decides when to switch from a restrictive to a non-restrictive quarantine and vice versa. We show that, if travel between countries is frequent, then the policy dictated by multiple governments is sub-optimal. But if international travel is restricted, then the policy may become optimal.

Modelling optimal lockdowns with waning immunity

This paper studies continuing optimal lockdowns (can also be interpreted as quarantines or self-isolation) in the long run if a disease (Covid-19) is endemic and immunity can fail, that is, the disease has SIRS dynamics. We model how disease related mortality affects the optimal choices in a dynamic general equilibrium neoclassical growth framework. An extended welfare function that incorporates loss from mortality is used. In a disease endemic steady state, without this welfare loss even if there is continuing mortality, it is not optimal to impose even a partial lockdown. We characterize how the optimal restriction and equilibrium outcomes vary with the effectiveness of the lockdown, the productivity of working from home, the rate of mortality from the disease, and failure of immunity. We provide the sufficiency conditions for economic models with SIRS dynamics with disease related mortality-a class of models which are non-convex and have endogenous discounting so that no existing results are applicable.

Worry and fear during the COVID-19 epidemic normalization: Analyzing prevalence and risk factors among 182,991 high school students in China

It is worth paying attention to the level of worry and fear of students during the normalization of the epidemic. We conducted the cross-sectional study that included 182,991 high school students using an online survey to assess worry and fear levels and its influencing factors in three cities in Henan Province, China, from May 21 to May 27, 2021. Worry and fear levels were measured using a five-item Likert-type scale. Multinomial logistic regression was used to analyze the risk factors. Approximately 65.42% of students reported being high worry about the coronavirus disease 2019 (COVID-19) epidemic, and about 41.44% of students reported being high fear about the COVID-19 epidemic. In addition, compared with female, male had lower odds of suffering from a high worry, and compared with senior high school students, junior high school students were more likely to suffer from a high worry. Students lived in rural had highest odds increased likelihood of high worry. Similar results in fear levels. During the normalization period of the COVID-19 epidemic, many students have high levels of worry and fear. This study has implications for potential influencers of worry and fear levels in follow-up work.

Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density-a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can 'jump' (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

Public health ethics: critiques of the "new normal"

The global response to the recent coronavirus pandemic has revealed an ethical crisis in public health. This article analyses key pandemic public health policies in light of widely accepted ethical principles: the need for evidence, the least restrictive/harmful alternative, proportionality, equity, reciprocity, due legal process, and transparency. Many policies would be considered unacceptable according to pre-pandemic norms of public health ethics. There are thus significant opportunities to develop more ethical responses to future pandemics. This paper serves as the introduction to this Special Issue of Monash Bioethics Review and provides background for the other articles in this collection.

Comparison of COVID-19 Vaccine Policies in Italy, India, and South Africa

(1) Purpose: This study aimed to analyze coronavirus disease 2019 (COVID-19) vaccine policies and their effectiveness in Italy, India, and South Africa to provide empirical experience for vaccination and COVID-19 pandemic control. (2) Methods: The study systematically summarized the COVID-19 vaccine policies in Italy, India, and South Africa through public information available on the official websites of the World Health Organization and the ministries of health in these three countries. Total vaccinations, COVID-19 vaccination rates, rates of fully vaccinated, rates of booster-vaccinated, and total confirmed cases were selected for cross-sectional comparison of COVID-19 vaccination in these three countries. Daily cases per million, daily deaths per million, and the effective reproduction rate were calculated to measure the effectiveness of COVID-19 vaccine policies implementation in each of these three countries. (3) Results: Italy, India, and South Africa differ in the start date of COVID-19 vaccination, vaccine types, vaccine appointments, and whether vaccinations are free. The COVID-19 vaccination rates in these three countries varied widely, with Italy having the highest and South Africa the lowest. COVID-19 vaccination has had a positive effect on reducing daily deaths and stabilizing the effective reproduction rate. The three countries had experienced more than one outbreak spike due to the spread of new mutated strains since the start of COVID-19 vaccination. (4) Conclusions: This study concluded that responding to the COVID-19 pandemic requires active promotion of basic and booster vaccinations to comprehensively build up the population immune barrier. Promoting equitable distribution of COVID-19 vaccine internationally and solidarity and cooperation among countries maximizes global common interests. By combining vaccination with non-pharmaceutical interventions, the pandemic can be prevented and controlled comprehensively and systematically in three aspects: detection of the source of infection, reduction of transmission routes, and protection of susceptible populations.

An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: The case study of Lombardy region

BACKGROUND

In Italy, the administration of COVID-19 vaccines began in late 2020. In the early stages, the number of available doses was limited. To maximize the effectiveness of the vaccine campaign, the national health agency assigned priority access to at-risk individuals, such as health care workers and the elderly. Current vaccination campaign strategies do not take full advantage of the latest mathematical models, which capture many subtle nuances, allowing different territorial situations to be analyzed aiming to make context-specific decisions.

OBJECTIVES

The main objective is the definition of an agent-based model using open data and scientific literature to assess and optimize the impact of vaccine campaigns for an Italian region. Specifically, the aim is twofold: (i) estimate the reduction in the number of infections and deaths attributable to vaccines, and (ii) assess the performances of alternative vaccine allocation strategies.

METHODS

The COVID-19 Agent-based simulator Covasim has been employed to build an agent-based model by considering the Lombardy region as case study. The model has been tailored by leveraging open data and knowledge from the scientific literature. Dynamic mobility restrictions and the presence of Variant of Concern have been explicitly represented. Free parameters have been calibrated using the grid search methodology.

RESULTS

The model mimics the COVID-19 wave that hit Lombardy from September 2020 to April 2021. It suggests that 168,492 cumulative infections 2,990 cumulative deaths have been avoided due to the vaccination campaign in Lombardy from January 1 to April 30, 2021. Without vaccines, the number of deaths would have been 66% greater in the 80-89 age group and 114% greater for those over 90. The best vaccine allocation strategy depends on the goal. To minimize infections, the best policy is related to dose availability. If at least 1/3 of the population can be covered in 4 months, targeting at-risk individuals and the elderly first is recommended; otherwise, the youngest people should be vaccinated first. To minimize overall deaths, priority is best given to at-risk groups and the elderly in all scenarios.

CONCLUSIONS

This work proposes a methodological approach that leverages open data and scientific literature to build a model of COVID-19 capable of assessing and optimizing the impact of vaccine campaigns. This methodology can help national institutions to design regional mathematical models that can support pandemic-related decision-making processes.

Adaptive network modeling of social distancing interventions

The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.

Robust optimal control of compartmental models in epidemiology: Application to the COVID-19 pandemic

In this paper, a spectral approach is used to formulate and solve robust optimal control problems for compartmental epidemic models, allowing the uncertainty propagation through the optimal control model to be represented by a polynomial expansion of its stochastic state variables. More specifically, a statistical moment-based polynomial chaos expansion is employed. The spectral expansion of the stochastic state variables allows the computation of their main statistics to be carried out, resulting in a compact and efficient representation of the variability of the optimal control model with respect to its random parameters. The proposed robust formulation provides the designers of the optimal control strategy of the epidemic model the capability to increase the predictability of the results by simply adding upper bounds on the variability of the state variables. Moreover, this approach yields a way to efficiently estimate the probability distributions of the stochastic state variables and conduct a global sensitivity analysis. To show the practical implementation of the proposed approach, a mathematical model of COVID-19 transmission is considered. The numerical results show that the spectral approach proposed to formulate and solve robust optimal control problems for compartmental epidemic models provides healthcare systems with a valuable tool to mitigate and control the impact of infectious diseases.

Identification of lymphocyte subgroups with flow cytometry in COVID-19 patients

Objective: We aimed to determine lymphocyte subgroups and activation status of flow cytometry in COVID-19 patients and examine their relationship with disease stage and length of hospital stay. Material and Method: Forty patients were analyzed in this study and compared with the age and sex-matched 40 healthy controls. COVID-19 patients have split as early and advanced-stage diseases. Flow cytometry assay was performed to determine the counts of lymphocyte subsets and activation status. Total lymphocyte count was calculated and CD45 (cluster of differentiation), CD3, CD4, CD8, CD19, CD27, CD38, CD56, CD57, and IgD were studied on lymphocyte gate. T helper / T cytotoxic rates and length of hospital stay were recorded. Results: The patients' CD3(+)CD4(+) ( T helper) count and CD27 expression on T cells counts were significantly lower, and CD57 expression on CD3(+)CD8(+) T cytotoxic cells were significantly higher (p

Does the rubella immunoglobulin G affect the severity of COVID- 19? : Rubella immunoglobulin G and COVID- 19

OBJECTIVE & AIM

The coronavirus disease, so far (COVID-19) has brought about millions of infections and fatalities throughout the world. Our aim was to determine the correlation between rubella IGG titers with the severity COVID-19.  MATERIALS & METHODS: This study was conducted among COVID-19 confirmed patients over 18 years of age. The disease severity levels were categorized by WHO interim guidance. The rubella-specific IgG antibody-titer spectrum was measured (within first 48 h of hospitalization) by enzyme-linked immunosorbent assay (ELISA).

RESULT

In a study of 46 inpatients with varying COVID-19 disease severity (mild, moderate, severe, and critical), we observed a negative correlation between rubella IgG antibody titers and COVID-19 severity (P-Value = 0.017), There was an interaction between COVID-19 vaccination history and rubella IGG on severity COVID-19 (P-Value = 0.0015). There was an interaction between age group under 44 years (including national measles- rubella (MR) vaccination in Iran) and rubella IGG titers on severity COVID-19 too (p-value = 0.014).

CONCLUSION

In conclusion, MR vaccination seems to have a positive effect in reducing the severity of the disease, emphasizing that, the important and separate effect of the IGG rubella (due to natural or extrinsic immunity) titers is determining.

A New Compartment Model of COVID-19 Transmission: The Broken-Link Model

We propose a new compartment model of COVID-19 spread, the broken-link model, which includes the effect from unconnected infectious links of the transmission. The traditional SIR-type epidemic models are widely used to analyze the spread status, and the models show the exponential growth of the number of infected people. However, even in the early stage of the spread, it is proven by the actual data that the exponential growth did not occur all over the world. We presume this is caused by the suppression of secondary and higher-order transmissions of COVID-19. We find that the proposed broken-link model quantitatively describes the mechanism of this suppression, which leads to the shape of epicurves of confirmed cases are governed by the probability of unconnected infectious links, and the magnitudes of the cases are proportional to expR0 in each infectious surge generated by a virus of the basic reproduction number R0, and is consistent with the actual data.

Effects of digital devices and online learning on computer vision syndrome in students during the COVID-19 era: an online questionnaire study

PURPOSE

Computer vision syndrome (CVS) describes a group of eye and vision-related problems that result from prolonged digital device use. This study aims to assess the prevalence and associated factors of CVS among students during the lockdown resulting from the COVID-19 pandemic.

METHODS

A cross-sectional, online, questionnaire-based study performed among high school students in Thailand.

RESULTS

A total of 2476 students, with mean age of 15.52±1.66 years, were included in this study. The mean number of hours of digital device use per day (10.53±2.99) increased during the COVID-19 pandemic compared with before its advent (6.13±2.8). The mean number of hours of online learning was 7.03±2.06 hours per day during the pandemic. CVS was found in 70.1% of students, and its severity correlated with both the number of hours of online learning and the total number of hours of digital device usage (p<0.001). Multiple logistic regression analysis revealed that the factors associated with CVS included age ≤15 years (adjusted OR (AOR)=2.17), overall digital device usage >6 hours per day (AOR=1.91), online learning >5 hours per day (AOR=4.99), multiple digital device usage (AOR=2.15), refractive errors (AOR=2.89), presence of back pain (AOR=2.06) and presence of neck pain (AOR=2.36).

CONCLUSIONS

The number of hours of digital device usage increased during lockdown. Over 70% of children had CVS, whose associated factors, including hours of digital device usage, hours of online learning, ergonomics and refractive errors, should be adjusted to decrease the risk of acquiring this condition. Online learning will remain, along with CVS, after this pandemic, and we hope our research will be taken into account in remodelling our education system accordingly.

Excesso de Mortalidade Hospitalar por Doenças Cardiovasculares no Brasil Durante o Primeiro Ano da Pandemia de COVID-19

Fundamento:

A pandemia da COVID-19 tem causado um impacto sobre a mortalidade por várias doenças em todo o mundo, especialmente por doenças cardiovasculares (DCVs). O Brasil é um país de dimensões continentais com diferenças significativas na estrutura de saúde entre seus estados.

Objetivo:

Analisar a mortalidade hospitalar por DCV no sistema público de saúde durante o primeiro ano da pandemia por COVID-19 (2020) no Brasil.

Métodos:

Este é um estudo ecológico analisando o número absoluto de mortes hospitalares e a taxa de mortalidade hospitalar no Brasil, suas macrorregiões, e unidades federativas. Os dados foram obtidos do Sistema de Informações Hospitalares (SIH) do Ministério da Saúde. O P-escore foi usado para analisar o excesso de mortalidade. O escore compara os eventos observados com os eventos esperados para um dado local e período. O escore-P foi corrigido por um modelo de regressão joinpoint, com um intervalo de confiança de 95% e nível de significância de 5%.

Resultados:

Houve 93.104 óbitos hospitalares por DCV no Brasil em 2020, o que representa 1495 menos óbitos (escore-P: -1,58) que o esperado. A região centro-oeste apresentou um escore-P positivo, com um aumento de 15,1% no número de mortes. Dez estados apresentaram um maior número de óbitos em 2020. Ainda, observou-se um excesso de 13,3% de mortalidade hospitalar no país como um todo, e um excesso de mortalidade hospitalar em todas as macrorregiões.

Conclusões:

Houve uma diminuição no número absoluto de óbitos hospitalares, bem como um aumento na taxa de mortalidade por DCV no Brasil em 2020, após o início da pandemia por COVID-19.

Impact of COVID-19 social distancing on medical research from the perspective of postgraduate students: a cross-sectional online survey

Objective

To investigate the impact of COVID-19 social distancing on medical research from the perspective of postgraduate students.

Methods

A cross-sectional study using an online survey was conducted from October 31 to November 1, 2021. A questionnaire was used to assess the impact of COVID-19 social distancing on medical research among postgraduate students. The questionnaire included basic information, medical research information, and information about social distancing measures. Participants also completed the self-made Research Work Affected Scale of Postgraduates (RWAS-P; qualitative evaluation: very mildly 0-10; mildly 11-20; moderately 21-30; severely 31-40; very severely 41-50). Logistic regression was used to identify factors related to the impact of COVID-19 social distancing.

Results

A total of 468 participants were analyzed; 95.2% of the participants adhered to social distancing measures. The median total RWAS-P score was 22. The median RWAS-P scores for earlier research data, current research projects, future research plans, paper publication, and graduation schedule were 2, 6, 6, 6, and 4, respectively (score range 0-10). The higher grade of students, experimental research, and existence of inappetence or sleeplessness were related to negative attitude towards COVID-19 social distancing (odd ratio = 6.35, 9.80, 2.31, 2.15, 1.95, respectively).

Conclusions

Participants reported that social distancing had a moderate overall impact on their medical research. Social distancing had the greatest impact on current research projects, future research plans, and paper publications among postgraduate students. Higher grade level, experimental research type, inappetence, and sleeplessness were related to the impact of social distancing on their medical research.

Prediction and prevention of pandemics via graphical model inference and convex programming

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any [Formula: see text] of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the [Formula: see text] state and a node which remained safe is in the [Formula: see text] state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-state the Prevention Challenge as the following tractable convex programming: for the bare Ising Model with pair-wise and bias factors representing the system without prevention measures, such that the MAP state is fully infected for at least one of the initial infection patterns, find the closest, for example in [Formula: see text], [Formula: see text] or any other convexity-preserving norm, therefore prevention-optimal, set of factors resulting in all the MAP states of the Ising model, with the optimal prevention measures applied, to become safe. We have illustrated efficiency of the scheme on a quasi-realistic model of Seattle. Our experiments have also revealed useful features, such as sparsity of the prevention solution in the case of the [Formula: see text] norm, and also somehow unexpected features, such as localization of the sparse prevention solution at pair-wise links which are NOT these which are most utilized/traveled.

Enhancing long-term forecasting: Learning from COVID-19 models

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

Long-term projections of COVID-19 trajectory have been used to inform various policies and decisions such as planning intensive care capacity, selecting clinical trial locations, and deciding on economic policy packages. However, these types of long-term forecasts are challenging as epidemics are complex: they include reinforcing contagion mechanisms that create exponential growth, are moderated by randomness in environmental and social determinants of transmission, and are subject to endogenous human responses to evolving risk perceptions. In this study we take a step towards systematically examining the modeling choices that regulate COVID-19 forecasting accuracy in two complementary studies. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. Second, we design a very simple forecasting model that only incorporates the key features identified in the first study, and show that the long-term prediction accuracy of this model is comparable with the best models in the CDC set. We conclude that forecasting models responding to future epidemics would benefit from starting small: first incorporating key mechanistic features, important behavioral feedbacks, and simple state-resetting approaches and then expanding to capture other features. Our study shows that the key to the long-term predictive power of epidemic models is an endogenous representation of human behavior in interaction with the evolving epidemic.

Physical Activity and Exercise Addiction During the Covid-19 Pandemic in Italy

Severe restrictive measures were implemented globally to limit the spread of the Covid-19 pandemic leading to significant lifestyle changes and impacting on both the physical and the mental health of citizens. Caught by the fear of getting sick, some individuals have adopted behaviors which favored the development of exercise addiction (EA). Our aim was to evaluate physical activity habits and the risk of EA in the general Italian population during phase 1 of the lockdown. The role of appearance anxiety (AA), self-compassion, and use of performance and image enhancing drugs (PIEDs) as predictors of EA development were investigated. A comparison between physically active subjects with the inactive ones was also included. Between April and May 2020, an online survey was conducted across Italy. Nine hundred thirty-six answers were collected. The rate of EA in the physically active sample (782 subjects) was 4.1%. The physically active group showed higher SCS scores and a greater use of PIEDs. Of the physically active participants, 84.2% reported variations in their fitness routine. Perceived benefit of exercising resulted significantly higher in those with EA. Subjects with EA reported stronger motivation in engaging in physical activity as for "physical wellness," "psychological well-being," and "sexual attractiveness and confidence in relationship." A higher level of AA, a lower level of self-compassion, and a higher perceived benefit of exercising during lockdown were all significant predictors for the presence of EA. Our findings suggest that the fear of getting sick from Covid-19, combined with radical changes in the lifestyles induced by the lockdown and individual personological characteristics, can favor the development of EA and related phenomena in the general population.

Public acceptability of non-pharmaceutical interventions to control a pandemic in the UK: a discrete choice experiment

OBJECTIVE

To understand how individuals trade off between features of non-pharmaceutical interventions (eg, lockdowns) to control a pandemic across the four nations of the UK.

DESIGN

A survey that included a discrete choice experiment. The survey design was informed using policy documents, social media analysis and input from remote think-aloud interviews with members of the public (n=23).

SETTING

A nationwide survey across the four nations of the UK using an online panel between 29 October and 12 December 2020.

PARTICIPANTS

Individuals who are over 18 years old. A total of 4120 adults completed the survey (1112 in England, 848 in Northern Ireland, 1143 in Scotland and 1098 in Wales).

PRIMARY OUTCOME MEASURE

Adult's preferences for, and trade-offs between, type of lockdown restrictions, length of lockdown, postponement of routine healthcare, excess deaths, impact on the ability to buy things and unemployment.

RESULTS

The majority of adults are willing to accept higher excess deaths if this means lockdowns that are less strict, shorter and do not postpone routine healthcare. On average, respondents in England were willing to accept a higher increase in excess deaths to have less strict lockdown restrictions introduced compared with Scotland, Northern Ireland and Wales, respectively. In all four countries, one out of five respondents were willing to reduce excess deaths at all costs.

CONCLUSIONS

The majority of the UK population is willing to accept the increase in excess deaths associated with introducing less strict lockdown restrictions. The acceptability of different restriction scenarios varies according to the features of the lockdown and across countries. Governments can use information about trade-off preferences to inform the introduction of different lockdown restriction levels and design compensation policies that maximise societal welfare.

Association of Non-Pharmaceutical Interventions to Reduce the Spread of SARS-CoV-2 With Anxiety and Depressive Symptoms: A Multi-National Study of 43 Countries

Objectives: To examine the association of non-pharmaceutical interventions (NPIs) with anxiety and depressive symptoms among adults and determine if these associations varied by gender and age. Methods: We combined survey data from 16,177,184 adults from 43 countries who participated in the daily COVID-19 Trends and Impact Survey via Facebook with time-varying NPI data from the Oxford COVID-19 Government Response Tracker between 24 April 2020 and 20 December 2020. Using logistic regression models, we examined the association of [1] overall NPI stringency and [2] seven individual NPIs (school closures, workplace closures, cancellation of public events, restrictions on the size of gatherings, stay-at-home requirements, restrictions on internal movement, and international travel controls) with anxiety and depressive symptoms. Results: More stringent implementation of NPIs was associated with a higher odds of anxiety and depressive symptoms, albeit with very small effect sizes. Individual NPIs had heterogeneous associations with anxiety and depressive symptoms by gender and age. Conclusion: Governments worldwide should be prepared to address the possible mental health consequences of stringent NPI implementation with both universal and targeted interventions for vulnerable groups.

Real-time pandemic surveillance using hospital admissions and mobility data

Forecasting COVID-19 healthcare demand has been hindered by poor data throughout the pandemic. We introduce a robust model for predicting COVID-19 transmission and hospitalizations based on COVID-19 hospital admissions and cell phone mobility data. This approach was developed by a municipal COVID-19 task force in Austin, TX, which includes civic leaders, public health officials, healthcare executives, and scientists. The model was incorporated into a dashboard providing daily healthcare forecasts that have raised public awareness, guided the city’s staged alert system to prevent unmanageable ICU surges, and triggered the launch of an alternative care site to accommodate hospital overflow.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

A Comparative Retrospective Study of COVID-19 Responses in Four Representative Asian Countries

Purpose

This study compared the government policies and non-pharmaceutical interventions adopted by South Korea, Japan, India, and China in response to COVID-19 during 2020–2021 and assessed their effectiveness. We hope that our research will help control the COVID-19 waves and a future crisis of this nature.

Methods

COVID-19 case data were obtained from Our World in Data database. Combined with case data, we made a retrospective study by analyzing the government policies and non-pharmaceutical interventions taken during this pandemic in these four representative Asian countries (South Korea, Japan, India, and China).

Results

From January 2020 to May 18, 2021, South Korea and Japan experienced three waves of COVID-19 outbreaks, but the number of daily new confirmed cases per million people was relatively small in both countries, and South Korea had fewer daily new confirmed cases per million than Japan. Following the COVID-19 outbreak in Wuhan in late 2019, China successfully contained the first wave of the outbreak and was not currently experiencing a large-scale resurgence of the epidemic (Until May 18, 2021). India is experiencing a grim second wave of the epidemic, with far more daily new confirmed cases per million people than South Korea and Japan.

Conclusion

Successful practices in China and South Korea show that case identification and management, coupled with close contact tracing and isolation, is a powerful strategy. The lessons of Japan and India show that social distancing is an effective measure, but only if it is rigor and persistent. Finally, in both developed and developing countries, the development of health care systems and coordinated government leadership play a key role in overcoming epidemics.

Comparative Study of Government Response Measures and Epidemic Trends for COVID-19 Global Pandemic

The ongoing novel coronavirus (COVID-19) epidemic has evolved into a full range of challenges that the world is facing. Health and economic threats caused governments to take preventive measures against the spread of the disease. This study aims to provide a correlation analysis of the response measures adopted by countries and epidemic trends since the COVID-19 outbreak. This analysis picks 13 countries for quantitative assessment. We select a trusted model to fit the epidemic trend curves in segments and catch the characteristics based on which we explore the key factors of COVID-19 spread. This review generates a score table of government response measures according to the Likert scale. We use the Delphi method to obtain expert judgments about the government response in the Likert scale. Furthermore, we find a significant negative correlation between the epidemic trend characteristics and the government response measure scores given by experts through correlation analysis. More stringent government response measures correlate with fewer infections and fewer waves in the infection curves. Stringent government response measures curb the spread of COVID-19, limit the number of total infectious cases, and reduce the time to peak of total cases. The clusters of the results categorize the countries into two specific groups. This study will improve our understanding of the prevention of COVID-19 spread and government response.

Impact of information intervention on stochastic hepatitis B model and its variable-order fractional network

This paper aims at analyzing the dynamical behavior of a SIR hepatitis B epidemic stochastic model via a novel approach by incorporating the effect of information interventions and random perturbations. Initially, we demonstrate the positivity and global existence of the solutions. Afterward, we derive the stochastic threshold parameter R s , followed by the fact that this number concludes the transmission of hepatitis B from the population. By increasing the intensity of noise, we get R s less than one, inferring that ultimately hepatitis B will lapse. While decreasing the intensity of noise to a sufficient level, we have R s > 1 . For the case R s > 1 , adequate results for the presence of stationary distribution are achieved, showing the prevalence of hepatitis B. The present study also involves the derivation of the necessary conditions for the persistence of the epidemic. Finally, the main theoretical solutions are plotted through simulations. Discussion on theoretical and numerical results shows that utilizing random perturbations and information interventions have a pronounced impact on the syndrome's dynamics. Furthermore, since most communities interact with each other, and the disease spread rate is affected by this factor, a new variable-order fractional network of the stochastic hepatitis B model is offered. Subsequently, this study will provide a robust theoretical basis for comprehending worldwide SIR stochastic and variable-order fractional network-related case studies.

Who is more likely to hesitate to accept COVID-19 vaccine: a cross-sectional survey in China

BACKGROUND

The aim of our study was to identify factors associated with coronavirus disease 2019 (COVID-19)vaccine willingness in China to aid future public health actions to improve vaccination.

RESEARCH DESIGN AND METHODS

This study was conducted in August 2020 using a mixed-method approach, including a cross-sectional self-administered anonymous questionnaire survey and in-depth interviews with community residents in China.

RESULTS

: Of the participants, 30.9% showedCOVID-19 vaccine hesitancy. Being female(OR=1.297), having poor health(OR=1.312), having non-health or medical-related occupations (OR=1.129), no COVID-19 infection experience(OR=1.523), living with vulnerable family members(OR=1.294), less knowledge(OR=1.371), less attention to COVID-19 information(OR=1.430), less trust in official media(OR=1.336), less perceived susceptibility to COVID-19(OR=1.367), and less protective behavior(OR=1.195) were more likely to hesitate. Qualitative research has shown that they doubt the importance and necessity, as well as the effectiveness and safety of the vaccination. The economic and service accessibility of the vaccination was an impediment to their vaccine acceptance.

CONCLUSION

Nearly one-thirdof people showed hesitancy to accept COVID-19 vaccination in China. Our findings highlight that health communication and publicity should be performed for the targeted population, and immunization programs should be designed to remove underlying barriers to vaccine uptake.

Synchronization in epidemic growth and the impossibility of selective containment

Containment, aiming to prevent the epidemic stage of community-spreading altogether, and mitigation, aiming to merely ‘flatten the curve’ of a wide-ranged outbreak, constitute two qualitatively different approaches to combating an epidemic through non-pharmaceutical interventions. Here, we study a simple model of epidemic dynamics separating the population into two groups, namely a low-risk group and a high-risk group, for which different strategies are pursued. Due to synchronization effects, we find that maintaining a slower epidemic growth behaviour for the high-risk group is unstable against any finite coupling between the two groups. More precisely, the density of infected individuals in the two groups qualitatively evolves very similarly, apart from a small time delay and an overall scaling factor quantifying the coupling between the groups. Hence, selective containment of the epidemic in a targeted (high-risk) group is practically impossible whenever the surrounding society implements a mitigated community-spreading. We relate our general findings to the ongoing COVID-19 pandemic.

Impact of COVID-19 lockdown on the incidence and patterns of toxic exposures and poisoning in Jordan: a retrospective descriptive study

OBJECTIVES

To describe the effect of the COVID-19 lockdown in Jordan (21 March 2020-21 May 2020) on the incidence and patterns of toxic exposures and poisoning as compared with the same period from the previous year (21 March 2019-21 May 2019).

DESIGN

A retrospective descriptive study.

METHODS

Call data sourced from Pharmacy One Poison Center from the lockdown period (21 March 2020-21 May 2020) and the same period during 2019 (21 March 2019-21 May 2019) were revised. In addition, a database was established and analysed.

RESULTS

We noticed that not only did calls increased, but there was also a noticeable change in call patterns. Calls increased by 91% (544 vs 285 calls) during the lockdown period. Drugs were the most common among types of exposure, and the most prevalent route of exposure was ingestion. There was a notable increase in ocular exposure by 550% (13 vs 2 cases). The majority of exposures were at home and there were no occupational exposures. We found an increase in household cleaner exposure among males and an increase in alcohol exposure in females. Children aged below 5 years are the most affected. Even though there is an increase in the total number of cases, severe cases decreased.

CONCLUSION

The lockdown effect on rates of toxic exposures was prominent, whether through the increase in calls or the change in patterns. As people spent more time at home, their exposure to toxic agents increased. Furthermore, cleaning recommendations led to the misuse of cleaning and disinfectant products, increasing exposures related to abating the COVID-19 infection.

In Vivo protection from SARS-CoV-2 infection by ATN-161 in k18-hACE2 transgenic mice

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an infectious disease that has spread worldwide. Current treatments are limited in both availability and efficacy, such that improving our understanding of the factors that facilitate infection is urgently needed to more effectively treat infected individuals and to curb the pandemic. We and others have previously demonstrated the significance of interactions between the SARS-CoV-2 spike protein, integrin α5β1, and human ACE2 to facilitate viral entry into host cells in vitro. We previously found that inhibition of integrin α5β1 by the clinically validated small peptide ATN-161 inhibits these spike protein interactions and cell infection in vitro. In continuation with our previous findings, here we have further evaluated the therapeutic potential of ATN-161 on SARS-CoV-2 infection in k18-hACE2 transgenic (SARS-CoV-2 susceptible) mice in vivo. We discovered that treatment with single or repeated intravenous doses of ATN-161 (1 mg/kg) within 48 h after intranasal inoculation with SARS-CoV-2 lead to a reduction of lung viral load, viral immunofluorescence, and improved lung histology in a majority of mice 72 h post-infection. Furthermore, ATN-161 reduced SARS-CoV-2-induced increased expression of lung integrin α5 and αv (an α5-related integrin that has also been implicated in SARS-CoV-2 interactions) as well as the C-X-C motif chemokine ligand 10 (Cxcl10), further supporting the potential involvement of these integrins, and the anti-inflammatory potential of ATN-161, respectively, in SARS-CoV-2 infection. To the best of our knowledge, this is the first study demonstrating the potential therapeutic efficacy of targeting integrin α5β1 in SARS-CoV-2 infection in vivo and supports the development of ATN-161 as a novel SARS-CoV-2 therapy.

Public health and expert failure

In a modern democracy, a public health system includes mechanisms for the provision of expert scientific advice to elected officials. The decisions of elected officials generally will be degraded by expert failure, that is, the provision of bad advice. The theory of expert failure suggests that competition among experts generally is the best safeguard against expert failure. Monopoly power of experts increases the chance of expert failure. The risk of expert failure also is greater when scientific advice is provided by only one or a few disciplines. A national government can simulate a competitive market for expert advice by structuring the scientific advice it receives to ensure the production of multiple perspectives from multiple disciplines. I apply these general principles to the United Kingdom's Scientific Advisory Group for Emergencies (SAGE).

Coronavirus lockdown and virus suppression: An international analysis

This paper analyses the effect of lockdown against the coronavirus which is one of the fastest growing threats in the world. We focus on three categories of lockdown and group four continents, Asia, America, Europe, and Africa together to assess the effectiveness of such a measure to contain the virus. We also look at a number of variables linked to the spread of the virus to determine the factors affecting the growth of new confirmed cases. We show evidence that countries in Europe are more likely to impose a national lockdown than any other continent. For the empirical analysis, we undertake the cross-sectional regression model, logistic regression model and logistic growth curve as a method to apply the data collected over the period March to June 2020 as this is the data available at the time this paper is composed. The empirical results of this paper indicate that countries which impose the strictest form of lockdown will result in a reduction in growth of new confirmed cases.

Coronavirus lockdown and virus suppression: An international analysis

This paper analyses the effect of lockdown against the coronavirus which is one of the fastest growing threats in the world. We focus on three categories of lockdown and group four continents, Asia, America, Europe, and Africa together to assess the effectiveness of such a measure to contain the virus. We also look at a number of variables linked to the spread of the virus to determine the factors affecting the growth of new confirmed cases. We show evidence that countries in Europe are more likely to impose a national lockdown than any other continent. For the empirical analysis, we undertake the cross-sectional regression model, logistic regression model and logistic growth curve as a method to apply the data collected over the period March to June 2020 as this is the data available at the time this paper is composed. The empirical results of this paper indicate that countries which impose the strictest form of lockdown will result in a reduction in growth of new confirmed cases.

Screening out anti-inflammatory or anti-viral targets in Xuanfei Baidu Tang through a new technique of reverse finding target

Traditional Chinese herbal compound prescription in Xuanfei Baidu Tang (XBT) has obvious effects in the treatment of COVID-19. However, its effective compounds and targets for the treatment of COVID-19 remain unclear. Computer-Aided Drug Design is used to virtually screen out the anti-inflammatory or anti-viral compounds in XBT, and predict the potential targets by Discovery Studio 2020. Then, we searched for COVID-19 targets using Genecards databases and Protein Data Bank (PDB) databases and compared them to identify targets that were common to both. Finally, the target we screened out is: TP53 (Tumor Protein P53). This article also shows that XBT in the treatment of COVID-19 works in a multi-link and overall synergistic manner. Our results will help to design the new drugs for COVID-19.

Epidemiological models and COVID-19: a comparative view

Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials-some real examples are invoked-we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

Optimal strategies for COVID-19 prevention from global evidence achieved through social distancing, stay at home, travel restriction and lockdown: a systematic review

BACKGROUND

Coronavirus disease (COVID-19) is a global public health agenda with high level of pandemicity. There is no effective treatment, but prevention strategies can alter the pandemic. However, the effectiveness of existing preventive measures and strategies is inconclusive. Therefore, this study aimed to review evidence related to COVID-19 prevention achieved through social distancing, stay at home, travel ban and lockdown in order to determine best practices.

METHODS/DESIGN

This review has been conducted in accordance with the PRISMA and Cochrane guideline. A systematic literature search of articles archived from major medical databases (MEDLINE, SCOPUS, CINAHL, PsycINFO, and Web of Science) and Google scholar was done. Observational and modeling researches published to date with information on COVID-19 prevention like social distancing, stay at home, travel ban and lockdown were included. The articles were screened by two experts. Risk of bias of included studies was assessed through ROBINS-I tool and the certainty of evidence was graded using the GRADE approach for the main outcomes. The findings were presented by narration and in tabular form.

RESULTS

A total of 25 studies was included in the review. The studies consistently reported the benefit of social distancing, stay at home, travel restriction and lockdown measures. Mandatory social distancing reduced the daily growth rate by 9.1%, contacts by 7-9 folds, median number of infections by 92% and epidemic resolved in day 90. Travel restriction and lockdown averted 70.5% of exported cases in china and doubling time was increased from 2 to 4 days. It reduced contacts by 80% and decreased the initial R0, and the number of infected individuals decreased by 91.14%. Stay at home was associated with a 48.6 and 59.8% reduction in weekly morbidity and fatality. Obligatory, long term and early initiated programs were more effective.

CONCLUSION

Social distancing, stay at home, travel restriction and lockdown are effective to COVID-19 prevention. The strategies need to be obligatory, initiated early, implemented in large scale, and for a longer period of time. Combinations of the programs are more effective. However, the income of individuals should be guaranteed and supported.

Estimation of the economic burden of COVID-19 using disability-adjusted life years (DALYs) and productivity losses in Kerala, India: a model-based analysis

OBJECTIVES

From the beginning of the COVID-19 pandemic, clinical practice and research globally have centred on the prevention of transmission and treatment of the disease. The pandemic has had a huge impact on the economy and stressed healthcare systems worldwide. The present study estimates disability-adjusted life years (DALYs), years of potential productive life lost (YPPLL) and cost of productivity lost (CPL) due to premature mortality and absenteeism secondary to COVID-19 in the state of Kerala, India.

SETTING

Details on sociodemographics, incidence, death, quarantine, recovery time, etc were derived from public sources and the Collective for Open Data Distribution-Keralam. The working proportion for 5-year age-gender cohorts and the corresponding life expectancy were obtained from the 2011 Census of India.

PRIMARY AND SECONDARY OUTCOME MEASURES

The impact of the disease was computed through model-based analysis on various age-gender cohorts. Sensitivity analysis was conducted by adjusting six variables across 21 scenarios. We present two estimates, one until 15 November 2020 and later updated to 10 June 2021.

RESULTS

Severity of infection and mortality were higher among the older cohorts, with men being more susceptible than women in most subgroups. DALYs for males and females were 15 954.5 and 8638.4 until 15 November 2020, and 83 853.0 and 56 628.3 until 10 June 2021. The corresponding YPPLL were 1323.57 and 612.31 until 15 November 2020, and 6993.04 and 3811.57 until 10 June 2021, and the CPL (premature mortality) were 263 780 579.94 and 41 836 001.82 until 15 November 2020, and 1 419 557 903.76 and 278 275 495.29 until 10 June 2021.

CONCLUSIONS

Most of the COVID-19 burden was contributed by years of life lost. Losses due to YPPLL were reduced as the impact of COVID-19 infection was lesser among the productive cohorts. The CPL values for individuals aged 40-49 years old were the highest. These estimates provide the data necessary for policymakers to work on reducing the economic burden of COVID-19 in Kerala.

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R_o=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

Changing readiness to mitigate SARS-CoV-2 steered long-term epidemic and social trajectories

Societal responses crucially shape the course of a pandemic, but are difficult to predict. Mitigation measures such as social distancing are here assumed to minimize a utility function that consists of two conflicting sub-targets, the disease related mortality and the multifaceted consequences of mitigation. The relative weight of the two sub-targets defines the mitigation readiness H, which entails the political, social, and psychological aspects of decision making. The dynamics of social and behavioral mitigation thus follows an adaptive rule, which in turn is mediated by a non-adaptive dynamics of H. This framework for social dynamics is integrated into an epidemiological model and applied to the ongoing SARS-CoV-2 pandemic. Unperturbed simulations accurately reproduce diverse epidemic and mitigation trajectories from 2020 to 2021, reported from 11 European countries, Iran, and 8 US states. High regional variability in the severity and duration of the spring lockdown and in peak mortality rates of the first SARS-CoV-2 wave can be explained by differences in the reconstructed readiness H. A ubiquitous temporal decrease of H has greatly intensified second and third waves and slowed down their decay. The unprecedented skill of the model suggests that the combination of an adaptive and a non-adaptive rule may constitute a more fundamental mode in social dynamics. Its implementation in an epidemic context can produce realistic long-term scenarios relevant for strategic planning, such as on the feasibility of a zero-infection target or on the evolutionary arms race between mutations of SARS-CoV-2 and social responses.