Contactless digital financial innovation and global contagious COVID-19 pandemic in low income countries: Evidence from Uganda

Since its outbreak, Covid-19 has led to upsurge in economic inactivity, leaving many households and firms without access to and use of basic services including financial services. Specifically, with the lockdown and curfew, most traditional bank branches remained closed, leaving households without access to quality, affordable, convenient, and safe financial services. This study aims to establish whether contactless digital financial innovation like mobile money can promote access to and use of financial services in the presence of pandemic positive emotions in low-income countries. SmartPLS 3.0 was used to construct the structural equation mediation model with bootstrap based on 2,737 valid responses. It was found that contactless digital financial innovation such as mobile money significantly promotes access to and use of financial services in low-income countries under pandemic situation. Additionally, the findings showed that the use of contactless digital financial innovation promotes Covid-19 standard operating procedures in low-income countries. Cognizant to the role of human behaviour in technology adoption and usage, the structural equation model with bootstrapping revealed a 4 percentage points improvement in Covid-19 standard operating procedures due to the use of contactless mobile money channel. Accordingly, the findings could be useful in the following ways: governments in low-income countries may use it to promote public health concern under pandemic situations. Mobile money can allow individuals to store, send, and receive money during situation of limited or no movements caused by pandemic health restrictions. Besides, the use of contactless digital financial innovation may promote digital commerce in low-income countries under the pandemic situation. Similarly, mobile money can be used to promote government-to-person, person-to-person, person-to-business, and business-to-person payments under emergency situations. The findings may also help governments in low-income countries to rethink about taxes levied on mobile money. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Health behaviors and subsequent mental health problems during the COVID-19 pandemic: A longitudinal analysis of adults in the UK.

Introduction: Public health mitigation policies aimed at slowing the spread of COVID-19 led to an increase in mental health problems (MHPs). This study examines the association between multiple pre-pandemic health behaviors and MHPs prior to, and during, the COVID-19 pandemic. Methods: We analyzed a representative population sample of 11,256 adults (aged 20-65 years) from Understanding Society-The UK Household Longitudinal Study. Baseline data from participants interviewed in 2017/2019 (wave 9) were linked to web surveys conducted during the COVID-19 pandemic. We used latent class analysis (LCA) to identify mutually exclusive health behavior (physical activity, alcohol consumption, eating habits and smoking tobacco) clusters by gender, and examined the sociodemographic correlates of each cluster. We assessed how pre-pandemic latent classes of health behaviors were associated with changes in MHPs during the pandemic using fixed effects regression models. Results: Three health behavior clusters were identified: positive (33%), moderate (24%), and high risk (43%), where similar behaviors clustered within individuals and sociodemographic circumstances. In particular, gender, age, migrant status and ethnicity were found to have strong associations with each cluster. Our results also demonstrated a clear association in MHPs with health behaviors both prior to, and during the pandemic. There were significant increases in MHPs between 2017/2019 and January 2021, with fluctuations coinciding with changes in public health mitigation policies. Assessments across the three clusters showed about 25.2%, 16.9%, and 0.7% increases in MHPs in the positive, moderate and high risk health behavior clusters, respectively. Discussion: This study shows that pre-pandemic health behaviors were significantly associated with mental health before and during the pandemic. Holistic policy interventions and promotions targeting multiple health behaviors may be an effective strategy to improve mental health in the pandemic recovery period.

Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic.

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Global COVID-19 case fatality rates influenced by inequalities in human development and vaccination rates.

Aim: COVID-19 has exerted distress on virtually every aspect of human life with disproportionate mortality burdens on older individuals and those with underlying medical conditions. Variations in COVID-19 incidence and case fatality rates (CFRs) across countries have incited a growing research interest regarding the effect of social factors on COVID-19 case-loads and fatality rates. We investigated the effect of population median age, inequalities in human development, healthcare capacity, and pandemic mitigation indicators on country-specific COVID-19 CFRs across countries and regions. Subject and methods: Using population secondary data from multiple sources, we conducted a cross-sectional study and used regional analysis to compare regional differences in COVID-19 CFRs as influenced by the selected indicators. Results: The analysis revealed wide variations in COVID-19 CFRs and the selected indicators across countries and regions. Mean CFR was highest for South America at 1.973% (± 0.742) and lowest for Oceania at 0.264% (± 0.107), while the Africa sub-region recorded the lowest scores for pandemic preparedness, vaccination rate, and other indicators. Population Median Age [0.073 (0.033 0.113)], Vaccination Rate [-3.3389 (-5.570.033 -1.208)], and Inequality-Adjusted Human Development Index (IHDI) [-0.014 (-0.023 -0.004)] emerged as statistically significant predictors of COVID-19 CFR, with directions indicating increasing Population Median Age, higher inequalities in human development and low vaccination rate are predictive of higher fatalities from COVID-19. Conclusion: Regional differences in COVID-19 CFR may be influenced by underlying differences in sociodemographic and pandemic mitigation indicators. Populations with wide social inequalities, increased population Median Age and low vaccination rates are more likely to suffer higher fatalities from COVID-19.

Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study.

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.

Capturing the Differential Impacts of Easing COVID-19 Restrictions: Application of a Hybrid Model of Entropy and TOPSIS

The paper aims to carry out the ordinal evaluation of 30 countries of North and South Americas, separately at two different points of time on 30th July 2020 and on 30th November 2020, on the basis of 13 select criteria. It also compares the changes in the relative rankings, if any, between these two points of time of the nations caused by changes in the pandemic mitigation strategy – i,e by easing the restrictions. The study has used the Multi-criteria Decision Analysis (MCDA) approach for evaluation. The data analysis part has two major sections. The first section assigns weights to all of the thirteen criteria using the Entropy method. The second section uses the TOPSIS method of MCDA. The assigned weights indicate that two of the least important criteria are the counts of daily new cases per million population and the daily new deaths per million populations. The rankings of most of the nations differ on 30th November, 2020 over that on 30th July 2020. Changes in the values of these two criteria, in fact, caused the changes in the ordinal rankings of the nations. These two parameters represent the outcome of the COVID-19 mitigation efforts put forth by the nations. It also establishes that the COVID-19 mitigation strategy really matters when it comes to the ordinal ranking and performance appraisal of the nations. The novelty of the paper is that for the first time, the MCDA technique is used to analyse the impact of policy intervention in pandemic mitigation. © 2022 International Journal of Mathematical, Engineering and Management Sciences. All rights reserved.

The interplay between cognitive and affective risks in predicting COVID-19 precautions: a longitudinal representative study of Americans.

OBJECTIVE: Cognitive risk figures prominently in models predicting health behaviors, but affective risk is also important. We examined the interplay between cognitive risk (personal likelihood of COVID-19 infection or death) and affective risk (worry about COVID-19) in predicting COVID-19 precautionary behaviors. We also examined how outbreak severity bias (overestimation of the severity of COVID-19 in one's community) predicted these outcomes. DESIGN: In a representative sample of U.S. adults (N = 738; Mage = 46.8; 52% women; 78% white), participants who had not had COVID-19 took two online surveys two weeks apart in April 2020. MAIN OUTCOME MEASURES: We assessed cognitive risk, affective risk, and outbreak severity bias at baseline and at follow-up two precaution variables: prevention behaviors (e.g. social distancing) and behavioral willingness (e.g. vaccinations). RESULTS: Overall, affective risk better predicted precautions than cognitive risk. Moreover, overestimating the severity of the outbreak predicted more affective risk (but not cognitive risk) and in turn more precautions. Additional analyses showed that when affective risk was lower (as opposed to higher) greater cognitive risk and outbreak severity bias both predicted more precautions. CONCLUSION: These findings illustrate the importance of affective risk and outbreak severity bias in understanding COVID-19 precautionary behavior.

Capturing the Differential Impacts of Easing COVID-19 Restrictions: Application of a Hybrid Model of Entropy and TOPSIS

The paper aims to carry out the ordinal evaluation of 30 countries of North and South Americas, separately at two different points of time on 30th July 2020 and on 30th November 2020, on the basis of 13 select criteria. It also compares the changes in the relative rankings, if any, between these two points of time of the nations caused by changes in the pandemic mitigation strategy - i,e by easing the restrictions. The study has used the Multi-criteria Decision Analysis (MCDA) approach for evaluation. The data analysis part has two major sections. The first section assigns weights to all of the thirteen criteria using the Entropy method. The second section uses the TOPSIS method of MCDA. The assigned weights indicate that two of the least important criteria are the counts of daily new cases per million population and the daily new deaths per million populations. The rankings of most of the nations differ on 30th November, 2020 over that on 30th July 2020. Changes in the values of these two criteria, in fact, caused the changes in the ordinal rankings of the nations. These two parameters represent the outcome of the COVID-19 mitigation efforts put forth by the nations. It also establishes that the COVID-19 mitigation strategy really matters when it comes to the ordinal ranking and performance appraisal of the nations. The novelty of the paper is that for the first time, the MCDA technique is used to analyse the impact of policy intervention in pandemic mitigation.

Connected Medical Kiosks to Counter COVID-19: Needs, Architecture & Design Guidelines

In several countries, the connected systems and especially the Internet of Medical Things (IoMT) based systems has been deployed with other advanced technologies to counter and to mitigate the spread of the coronavirus disease 2019 known as (COVID-19) and to smooth the severity of the pandemic. Accordingly, many IoMT- based systems merged with the use of blockchain, artificial intelligence and big data analytics proposed and adopted by diverse countries and governments to counter COVID-19 pandemic. In this context, this paper highlights the needs, the architecture, the design guidelines and the provided services related to our proposed system named (CMK-COVID: Connected Medical Kiosks to Counter COVID-19). Besides that, this paper offers useful insights into the literature related IoMT-based solutions countering COVID-19. © 2021 IEEE

Fear, Behavior, and the COVID-19 Pandemic: A City-Scale Agent-Based Model Using Socio-Demographic and Spatial Map Data

Modeling infectious diseases has been shown to be of great importance and utility during the ongoing COVID-19 pandemic. From today's globalized information landscape, however, a plethora of new factors arise that have not been covered in previous models. In this paper, we present an agent-based model that reflects the complex interplay between the spread of a pathogen and individual protective behaviors under the influence of media messaging. We use the Rescorla-Wagner model of associative learning for the growth and extinction of fear, a factor that has been proposed as a major contributor in the determination of protective behavior. The model space, as well as heterogeneous social structures among the agents, are created from empirical data. We incorporate factors like age, gender, wealth, and attitudes towards public health institutions. The model is able to reproduce the empirical trends of fear and protective behavior in Germany but struggles to simulate the accurate scale of disease spread. The decline of fear seems to promote a second wave of disease and the model suggests that individual protective behavior has a significant impact on the outcome of the epidemic. The influence of media in the form of messages promoting protective behavior is negligible in the model. Further research regarding factors influencing long-term protective behavior is recommended to improve communication and mitigation strategies.

On Robust Economic Control of Epidemics with Application to COVID-19

As of September 2021 the ongoing COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had already resulted in more than two hundred million cases infected and five million deaths worldwide. It is also causing tremendous economic losses to many nations. Governments in many countries are striving to salvage their economies. In this paper, we propose a new robust economic epidemic control problem that minimizes the worst medical/preventive costs under some epidemic control constraints. A simple SIRD-based epidemiology model with two uncertainties: The uncertain rate of infected cases which are undetected or asymptomatic and the uncertain effectiveness rate of control, is considered. With a linear control policy, we show sufficient conditions for the epidemic that is deemed to be “well-controlled”in the sense that infected cases go down to zero asymptotically and are upper bounded uniformly. Then we establish that the optimal linear policy that minimizes the medical/preventative costs. Finally, we also provide some numerical studies using the historical COVID-19 contagion data in Taiwan. A comparison with and without lockdown considerations is also provided. Author

COVID-19 and access to cancer care in Kenya: patient perspective.

COVID-19 disruptions severely impacted access to health services for noncommunicable diseases, including cancer, but few studies have examined patient perspectives of COVID-19-induced barriers to care in low/middle-income countries. Data come from a survey completed online, over the phone or in person of 284 adult people with cancer in Kenya. One-third (36%) of participants had primary or no education and 34% had some or complete secondary education. Half of the participants (49%) were aged 40 to 59, 21% were 18 to 39 and 23% were 60 or older. Two-thirds were female (65%) and most visited a national referral hospital in Nairobi to receive care (84%). Mean travel time to Nairobi from the respondent county of residence was 2.47 hours (±2.73). Most participants reported decreased household income (88%) and were worried about their ability to afford cancer treatment due to COVID-19 (79%). After covariate adjustment, participants who lost access to hospitals due to COVID-19 travel restrictions were 15 times more likely to experience a cancer care delay (OR = 14.90, 95% CI: 7.44-29.85) compared to those with continued access to hospitals. Every additional hour of travel time to Nairobi from their county of residence resulted in a 20% increase in the odds of a cancer care delay (OR = 1.20, 95% CI: 1.06-1.36). Transportation needs and uninterrupted access to cancer care and medicines should be accounted for in COVID-19 mitigation strategies. These strategies include permits for cancer patients and caregivers to travel past curfew time or through block posts to receive care during lockdowns, cash assistance and involving patient navigators to improve patient communication.

Optimal strategic pandemic control: human mobility and travel restriction.

This paper presents a model for finding optimal pandemic control policy considering cross-region human mobility. We extend the baseline susceptible-infectious-recovered (SIR) epidemiology model by including the net human mobility from a severely-impacted region to a mildly-affected region. The strategic optimal mitigation policy combining testing and lockdown in each region is then obtained with the goal of minimizing economic cost under the constraint of limited resources. We parametrize the model using the data of the COVID-19 pandemic and show that the optimal response strategy and mitigation outcome greatly rely on the mitigation duration, available resources, and cross-region human mobility. Furthermore, we discuss the economic impact of travel restriction policies through a quantitative analysis.

Presenting or Spinning Facts? Deconstructing the U.S. Centers for Disease Control Statement on the Importance of Reopening Schools Under COVID-19.

Credible, reliable and consistent information to the public, as well as health professionals and decision makers, is crucial to help navigate uncertainty and risk in times of crisis and concern. Traditionally, information and health communications issued by respected and established government agencies have been regarded as factual, unbiased and credible. The U.S. Centers for Disease Control and Prevention (CDC) is such an agency that addresses all aspects of health and public health on behalf of the U.S Government for the benefit of its citizens. In July 2020, the CDC issued guidelines on reopening schools which resulted in open criticism by the U.S. President and others, prompting a review and publication of revised guidelines together with a special "Statement on the Importance of Reopening Schools under COVID-19." We hypothesize that this statement introduced bias with the intention to shift the public perception and media narrative in favor of reopening of schools. Using a mixed methods approach, including an online text analysis tool, we demonstrate that document title and structure, word frequencies, word choice, and website presentation did not provide a balanced account of the complexity and uncertainty surrounding school reopening during the COVID-19 pandemic. Despite available scientific guidance and practical evidence-based advice on how to manage infection risks when reopening schools, the CDC Statement was intentionally overriding possible parent and public health concerns. The CDC Statement provides an example of how political influence is exercised over the presentation of science in the context of a major pandemic. It was withdrawn by the CDC in November 2020.

The microclimatic impacts of urban spaces on the behaviour of pandemics between propagation and containment: Case study historic Cairo.

Although previous researches proved that frequent visits to urban spaces enhance the physical and mental health of people, most governments adopted lockdown policies after the outbreak of COVID-19. This decision has negatively impacted the wellbeing of communities and the livability of urban spaces. In this context the research questions how far the microclimatic conditions of urban space would influence its performance during respiratory pandemics? The study investigated this question through a dense literature survey including 47 scientific journal articles and governmental reports. The outputs were synthesized through a quantitative assessment framework. It detected the spatio-environmental parameters influencing the behaviour of respiratory pandemics in urban settings. To validate the framework's outputs, the research applied case study sampling for 3 urban spaces in historic Cairo. It generated digital simulations and computations addressing solar radiation, natural ventilation, air temperature, and humidity, besides space dimension and number of users. The results illustrated the areas of adequate and poor microclimatic performance during pandemics. They are demonstrated through numerical tables, digital simulations, and graphs. Eventually, a concluding assessment framework selected the optimum urban space performance to be engaged in the public life of historic Cairo during lockdown periods.

Eviction, Health Inequity, and the Spread of COVID-19: Housing Policy as a Primary Pandemic Mitigation Strategy.

The COVID-19 pandemic precipitated catastrophic job loss, unprecedented unemployment rates, and severe economic hardship in renter households. As a result, housing precarity and the risk of eviction increased and worsened during the pandemic, especially among people of color and low-income populations. This paper considers the implications of this eviction crisis for health and health inequity, and the need for eviction prevention policies during the pandemic. Eviction and housing displacement are particularly threatening to individual and public health during a pandemic. Eviction is likely to increase COVID-19 infection rates because it results in overcrowded living environments, doubling up, transiency, limited access to healthcare, and a decreased ability to comply with pandemic mitigation strategies (e.g., social distancing, self-quarantine, and hygiene practices). Indeed, recent studies suggest that eviction may increase the spread of COVID-19 and that the absence or lifting of eviction moratoria may be associated with an increased rate of COVID-19 infection and death. Eviction is also a driver of health inequity as historic trends, and recent data demonstrate that people of color are more likely to face eviction and associated comorbidities. Black people have had less confidence in their ability to pay rent and are dying at 2.1 times the rate of non-Hispanic Whites. Indigenous Americans and Hispanic/Latinx people face an infection rate almost 3 times the rate of non-Hispanic whites. Disproportionate rates of both COVID-19 and eviction in communities of color compound negative health effects make eviction prevention a critical intervention to address racial health inequity. In light of the undisputed connection between eviction and health outcomes, eviction prevention, through moratoria and other supportive measures, is a key component of pandemic control strategies to mitigate COVID-19 spread and death.

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security.

In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve.

In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.