Culture and pandemic control at cross-roads: navigating the burial guidelines for COVID-19-related deaths in a Ghanaian setting.

BACKGROUND: Despite the large volume of scientific evidence on the rapid spread of the COVID-19 pandemic and associated high morbidity and mortality, little is known about the sociocultural disruptions which ensued. The current study explored the nuanced navigation of the COVID-19-related death and burial protocols and its impact on traditional burial and funeral rites in Ghana. METHODS: This qualitative study was based on the 'focused' ethnographic design. Data were collected using key informant interviews from nineteen COVID-19-related bereaved family members and public health officials involved in enforcing adherence to COVID-19-related death and burial protocols in the Cape Coast Metropolis of Central region of Ghana. Recursive analysis was conducted to generate the themes and sub-themes from the data. RESULTS: The overarching theme was "Uncultural" connotations ascribed to the COVID-19-related death and burial protocols. The COVID-19-related death and burial protocols were ubiquitously deemed by participants to be 'uncultural' as they inhibited deep-rooted indigenous and eschatological rites of separation between the living and the dead. This was fueled by limited awareness and knowledge about the COVID-19 burial protocols, resulting in fierce resistance by bereaved family members who demanded that public health officials release the bodies of their deceased relatives. Such resistance in the midst of resource limitation led to negotiated compromises of the COVID-19-related death and burial protocols between family members and public health officials. CONCLUSIONS: Insensitivity to socio-cultural practices compromised the implementation of the COVID-19 pandemic control interventions, particularly, the COVID-19-related death and burial protocols. Some compromises that were not sanctioned by the protocols were reached to allow health officials and families respectfully bury their dead. These findings call for the need to prioritize the incorporation of sociocultural practices in future pandemic prevention and management strategies.

Challenges and opportunities for improved contact tracing in Ghana: experiences from Coronavirus disease-2019-related contact tracing in the Bono region.

BACKGROUND: In Ghana, contact tracing received heightened attention in the fight against the COVID-19 pandemic during its peak period. Despite the successes achieved, numerous challenges continue to limit the efforts of contact tracing in completely curtailing the effect of the pandemic. Despite these challenges, there are still opportunities that could be harnessed from the COVID-19 contact tracing experience for future eventualities. This study thus identified the challenges and opportunities associated with COVID-19 contact tracing in the Bono Region of Ghana. METHODS: Using a focus group discussion (FGD) approach, an exploratory qualitative design was conducted in six selected districts of the Bono region of Ghana in this study. The purposeful sampling technique was employed to recruit 39 contact tracers who were grouped into six focus groups. A thematic content analysis approach via ATLAS ti version 9.0 software was used to analyse the data and presented under two broad themes. RESULTS: The discussants reported twelve (12) challenges that hindered effective contact tracing in the Bono region. These include inadequate personal protective equipment, harassment by contacts, politicisation of the discourse around the disease, stigmatization, delays in processing test results, poor remuneration and lack of insurance package, inadequate staffing, difficulty in locating contacts, poor quarantine practices, poor education on COVID-19, language barrier and transportation challenges. Opportunities for improving contact tracing include cooperation, awareness creation, leveraging on knowledge gained in contact tracing, and effective emergency plans for future pandemics. CONCLUSION: There is a need for health authorities, particularly in the region, and the state as a whole to address contact tracing-related challenges while simultaneously harnessing the recommended opportunities for improved contact tracing in the future for effective pandemic control.

Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data.

In the post-COVID-19 era, the pandemic response is increasingly difficult and entails a high cost to society. Existing pandemic control methods, such as lockdowns, greatly affect residents' normal lives. This paper proposes a pandemic control method, consisting of the scientific delineation of urban areas based on multimodal transportation data. An improved Leiden method based on the gravity model is used to construct a preliminary zoning scheme, which is then modified by spatial constraints. The modularity index demonstrates the suitability of this method for community detection. This method can minimize cut-off traffic flows between pandemic control areas. The results show that only 24.8% of travel links are disrupted using our method, which could reduce both the impact of pandemic control on the daily life of residents and its cost. These findings can help develop sustainable strategies and proposals for effective pandemic response.

Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19.

The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.

Digital cities and the spread of COVID-19: Characterizing the impact of non-pharmaceutical interventions in five cities in Spain.

Mathematical modeling has been fundamental to achieving near real-time accurate forecasts of the spread of COVID-19. Similarly, the design of non-pharmaceutical interventions has played a key role in the application of policies to contain the spread. However, there is less work done regarding quantitative approaches to characterize the impact of each intervention, which can greatly vary depending on the culture, region, and specific circumstances of the population under consideration. In this work, we develop a high-resolution, data-driven agent-based model of the spread of COVID-19 among the population in five Spanish cities. These populations synthesize multiple data sources that summarize the main interaction environments leading to potential contacts. We simulate the spreading of COVID-19 in these cities and study the effect of several non-pharmaceutical interventions. We illustrate the potential of our approach through a case study and derive the impact of the most relevant interventions through scenarios where they are suppressed. Our framework constitutes a first tool to simulate different intervention scenarios for decision-making.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace.

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates.

BACKGROUND AND OBJECTIVE: Besides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. METHODS: Here, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. RESULTS: The results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. CONCLUSIONS: The proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.

Path integral control of a stochastic multi-risk SIR pandemic model.

In this paper a Feynman-type path integral control approach is used for a recursive formulation of a health objective function subject to a fatigue dynamics, a forward-looking stochastic multi-risk susceptible-infective-recovered (SIR) model with risk-group's Bayesian opinion dynamics toward vaccination against COVID-19. My main interest lies in solving a minimization of a policy-maker's social cost which depends on some deterministic weight. I obtain an optimal lock-down intensity from a Wick-rotated Schrödinger-type equation which is analogous to a Hamiltonian-Jacobi-Bellman (HJB) equation. My formulation is based on path integral control and dynamic programming tools facilitates the analysis and permits the application of algorithm to obtain numerical solution for pandemic control model.

Accelerating COVID-19 testing: An experimental approach.

The COVID-19 pandemic, ever since its global outbreak in 2020, has continued to wreak havoc. Governments across the world were compelled to enforce strict nation-wide lockdowns, while emphasising on social distancing and quarantining suspected people in order to slow down the spread of the virus. During this time, there was a massive increase in demand for COVID-19 test kits. However, given the limited supply, countries were finding it hard to test enough people. This study proposes an approach called Encoded Blending (EB) to increase the number of tests drastically, without increasing the number of test kits. EB modifies the pooled testing method; this has been followed by countries like Germany, Israel and South Korea for mass testing their citizens. EB has the potential to reduce test kits requirement by up to 85% and 80% in a population with 5% and 10% affected cases, respectively.

Cost-effective Vaccine Provisioning using Coalitional Game Theory

Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.

Cost-effective Vaccine Provisioning using Coalitional Game Theory

Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach. © 2022 IEEE.

Challenges and perspectives of multi-virus biosensing techniques: A review.

In the context of globalization, individuals have an increased chance of being infected by multiple viruses simultaneously, thereby highlighting the importance of developing multiplexed devices. In addition to sufficient sensitivity and rapid response, multi-virus sensing techniques are expected to offer additional advantages including high throughput, one-time sampling for parallel analysis, and full automation with data visualization. In this paper, we review the optical, electrochemical, and mechanical platforms that enable multi-virus biosensing. The working mechanisms of each platform, including the detection principle, transducer configuration, bio-interface design, and detected signals, are reviewed. The advantages and limitations, as well as the challenges in implementing various detection strategies in real-life scenarios, were evaluated. Future perspectives on multiplexed biosensing techniques are critically discussed. Earlier access to multi-virus biosensors will efficiently serve for immediate pandemic control, such as in emerging SARS-CoV-2 and monkeypox cases.

A Security Games Inspired Approach for Distributed Control Of Pandemic Spread

Pandemics are a source of extensive mortality, economic impairment, and dramatic social fluctuation. Once a pandemic occurs, policymakers are faced with the highly challenging task of controlling it over time and space. In this article, a novel pandemic intervention policy that relies on the strategic deployment of inspection units (IUs) is proposed. These IUs are allocated in the environment, represented as a graph, and sample individuals who pass through the same node. If a sampled individual is identified as infected, she is extracted from the environment until she recovers (or dies). A realistic simulation-based evaluation of the Influenza A pathogen using both synthetic and real-world data is provided. The results demonstrate potential significant benefits of the proposed PIP in mitigating a pandemic spread which can complement other standard policies such as social distancing and mask-wearing.

TRANSPORTATION: THE AGATHOKAKOLOGICAL VEHICLE OF PANDEMIC TRANSMISSION AND MANAGEMENT

Discourses on pandemics often prioritize germane pharmaceutical issues while largely ignoring the social dimensions to them, especially the paradoxical role of transportation in pandemic transmission and management. Granted, from an epidemiological perspective, transportation is technically a vector of pandemics;however, by sustaining continuous critical supply chains and facilitating the conveyance of interventions during pandemics, transportation also plays a crucial role in pandemic control and management. Indeed, studies have shown that the risk of disease transmission can be significantly reduced by the observation of non-pharmaceutical transport protocols. Against this background, this paper highlights the dual roles of transportation in aiding and curbing pandemics, with a strong emphasis on the latter. Moreover, towards consolidating on the strengths of the transport sector in the control and management of public health issues, this paper underscores the sacrosanctity of responsible transport. Challenges were identified, and practicable recommendations were proffered towards containing them. © Scientific Journal of Silesian University of Technology. Series Transport.All rights reserved.

The resilience of national highway transportation in China under the COVID-19 outbreak

The global COVID-19 pandemic of 2020 affected travel patterns across the world. This chapter used data on the highway traffic volume to quantify and analyze the relationship between government restrictions and travel activity across 29 provinces in China. Space and time distributions of traffic volume across China during the first half of 2020 were used to quantify the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends was the impact of the government’s restriction policies on highway traffic volume as well as the relationship between traffic trends during the closure period and the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. A logarithm relationship between bounce level and recovery time revealed that a shorter recovery time leads to a higher bounce level. Based on these trends, policies that could shorten the recovery time and be beneficial to reboot traffic activity in the post-COVID-19 pandemic era can be identified.

Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world.

BACKGROUND: The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic. METHODS: In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region's SARS-COV-2 transmission dynamic. RESULTS: We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI's, over and above the ones identified in i) and ii). CONCLUSION: In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI's) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV.

COVID-19 pandemic: Analyzing of different pandemic control strategies using saturation models

Since December 2019, the world is confronted with the outbreak of the respiratory disease COVID-19. At the beginning of 2020, the COVID-19 epidemic evolved into a pandemic, which continues to this day. Within many countries, several control strategies or combinations of them, like restrictions (e.g. lockdown actions), medical care (e.g. development of vaccine or medicaments) and medical prevention (e.g. hygiene concept), were established with the goal to control the pandemic. Depending on the chosen control strategy, the COVID-19 spreading behavior slowed down or approximately stopped for a defined time range. This phenomenon is called saturation effect and can be described by saturation models: E.g. a fundamental approach is Verhulst (1838). The model parameter allows the interpretation of the spreading speed (growth) and the saturation effect in a sound way. This paper shows results of a research study of the COVID-19 spreading behavior and saturation effects depending on different pandemic control strategies in different countries and time phases based on Johns Hopkins University data base (2020). The study contains the analyzing of saturation effects related to short time periods, e.g. possible caused by lockdown strategies, geographical influences and medical prevention activities. The research study is focusing on reference countries like Germany, Japan, Denmark, Iceland, Ireland and Israel. © ESREL 2021. Published by Research Publishing, Singapore.

Patients' Responses to COVID-19 Pandemic: The Relationship Between Potential Pandemic-Induced Disruptions, Ontological Security, and Adaptive Responses in Taizhou, China.

Background: The COVID-19 pandemic has changed the social environment of most individuals around the world and has profoundly impacted people's lives, ontological security, and behavior. Among them, the patients are one of the groups most influenced by the pandemic. Objective: The present research aimed to study the relationship of COVID-19 pandemic-induced disruption to patients' daily lives, ontological security, and patients' responses to prevent the spread of COVID-19, and explore the role of ontological security. Methods: This article was based on an online structured questionnaire study conducted among hospitalized patients in Taizhou Hospital of Zhejiang Province, Taizhou, China, from 8 July to 11 August 2021. We analyzed the data using the multivariate regression model and mediation analysis method. Results: The results showed that the higher the pandemic-induced disruption to inpatients' lives, the better behavior would be taken by hospitalized patients to prevent the spread of COVID-19, and the perceived scarcity of ontological security played a mediating role in this process. Higher pandemic-induced disruption to patients' lives increased the ontological insecurity which further, in turn, reduced patients' good practice toward measures to prevent the novel coronavirus. Conclusion: These findings provided direct evidence for the relationship between pandemic-induced disruption, scarcity of ontological security, and patients' prevention behavior. It suggested that there was a need to emphasize patients' ontological security. Overall, these findings suggested that it is important to emphasize the mental health among patients during the COVID-19 pandemic, and implement strategies to offer psychological support when needed.

DATA ON ANTI-SARS-COV-2 ANTIBODIES (IGM/IGG) IN COVID-19 PATIENTS

SARS-CoV-2 is an enveloped RNA virus capable of suffering mutations when passing from animals to humans, mutations which will allow for longer persistence in circulation but possibly also greater infectivity. If the disease is detected, the identification of viral RNA in pathological products harvested from the respiratory tract by molecular tests (RT-PCR) is the absolute recommendation. Serological tests have a role in estimating the extent of the population's immunization. Due to the high risk of infra-community transmission, it is essential to implement a surveillance program of all possible virus transmission routes. The purpose of the study was in line with the current epidemiological situation, to evaluate a molecular tool for improving the control of the spread of SARS-CoV-2, for risk assessment and for the development of a protocol for the epidemiological management of the COVID- 19 pandemic. An advantage is the access to a considerable number of cases - Bucharest ranked 1st place in the number of confirmed cases of COVID-19 on 30.09.2021.