Spread of COVID-19 in urban neighbourhoods and slums of the developing world

Is income inequality linked to infectious disease prevalence? A hypothesis-generating study using tuberculosis

We study the association between infectious disease incidence and income inequality. We hypothesize that random social mixing in an income-unequal society brings into contact a) susceptible and infected poor and b) the infected-poor and the susceptible-rich, raising infectious disease incidence. We analyzed publicly available, country-level panel data for a large cross-section of countries between 1995 and 2013 to examine whether countries with elevated levels of income inequality have higher rates of pulmonary Tuberculosis (TB) incidence per capita. A "negative control" using anemia and diabetes (both non-communicable diseases and hence impervious to the hypothesized mechanism) is also applied. We find that high levels of income inequality are positively associated with tuberculosis incidence. All else equal, countries with income-Gini coefficients 10% apart show a statistically significant 4% difference in tuberculosis incidence. Income inequality had a null effect on the negative controls. Our cross-country regression results suggest that income inequality may create conditions where TB spreads more easily, and policy action to reduce income inequities could directly contribute to a reduced TB burden.

The impact of urban spatial environment on COVID-19: a case study in Beijing

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

A cross-sectional survey on the effectiveness of public health campaigns for changing knowledge, attitudes, and practices in Kenyan informal settlements during the COVID-19 pandemic

We performed two cross-sectional surveys across three informal settlements in Kenya (within Kisii county, Nairobi, and Nakuru county) to study the effectiveness of public health interventions during the COVID-19 pandemic. A total of 720 participants were surveyed from 120 randomly selected geographical locations (240 participants/settlement/survey), and a coordinated health promotion campaign was delivered between the two surveys by trained staff. Information relating to knowledge, attitudes, and practices (KAP) were collected by trained field workers using a validated questionnaire. The main outcomes showed improvements in: (i) mask-wearing (% of participants 'Always' using their mask increased from 71 to 74%, and the percentage using their masks 'Sometimes' decreased from 15% to 6%; p<0.001); (ii) practices related to face mask usage (% of subjects covering the mouth and nose increased from 91 to 95%, and those covering only part of their face decreased from around 2.5% to <1%; p<0.001). Significant improvements were also seen in the attitudes and expectations relating to mask wearing, and in the understanding of government directives. Over 50% of subjects in the post-campaign survey reported that social distancing was not possible in their communities and fears associated with COVID-19 testing were resistant to change (unchanged at 10%). Access to COVID-19 testing facilities was limited, leaving a large proportion of people unable to test. As willingness to take a COVID-19 test did not change between surveys (69 vs 70%; p = 0.57), despite increased availability, we recommend that policy level interventions are needed, aimed at mitigating adverse consequences of a positive test. Improvements of KAPs in the more crowded urban environment (Nairobi) were less than at settlements in rural or semi-urban settings (Nakuru and Kisii). We conclude that coordinated public health campaigns are effective in facilitating the change of KAPs amongst people living amidst challenging socio-economic conditions in informal settlements.

Vulnerability, neglect, and collectivity in Brazilian favelas: Surviving the threats of the COVID-19 pandemic and the state's necropolitics

The COVID-19 pandemic has deepened existing inequities and injustices in Brazil, seen in the disproportionately detrimental impacts on favelas. State policy responses to the pandemic have disregarded favela residents' experiences. Recommendations such as 'shelter-in-place' ignore the reality of over 11.4 million favela residents who cannot work from home or afford to stop working, nor practise physical distancing from others. This study investigates the discourse of community organisations in favelas as they respond to the threats of the COVID-19 pandemic and the state's necropolitics. Community organisations in favelas have taken action to protect their residents from the virus, unemployment and hunger. I assess organisations' (1) justification to act as a collective in their communities, and (2) stances about the government's responses to the crisis. Through content analysis of social media, websites and media appearances of eight favela organisations and collectives in São Paulo and Rio de Janeiro, this study finds three main themes through which organisations justify their actions: vulnerability, neglect, and collectivity and care. More than survival strategies, the actions of favela organisations are counter-political acts as they oppose the decrepit necropolitics of the state by collectively enduring in the Brazilian context of the COVID-19 pandemic. Understanding favela organisations' actions in response to the pandemic is fundamental. It further illuminates the impact of public health emergencies in the lives of informal settlements' residents and the governance of public health emergencies in these communities.

Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda

INTRODUCTION

Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions.

RESULTS

The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda.

CONCLUSION

The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

The Impact of the Pandemic on Poor Urban Neighborhoods: A Participatory Action Research Study of a "Favela" in Rio de Janeiro

The pandemic provoked by the coronavirus disease 2019 (COVID-19) devastated poor urban neighborhoods across the world, particularly in the Global South, although empirical data on this remain limited. In this article, the authors present data collected through a mixed-methods, participatory action research approach on the impacts of the pandemic in Cidade de Deus, a "favela," or poor informal settlement, in Rio de Janeiro. The authors find that the indirect consequences of COVID-19, in particular economic and mental health problems, were experienced as more severe than the direct effects of the virus itself, despite high rates of infection and mortality. The study also revealed that residents relied heavily on one another through local systems of mutual aid to address immediate crises. These findings suggest that the pandemic provoked a complex and diverse set of challenges and actions in the economic, social, physical, and mental spheres of poor urban neighborhoods.

Can you lock down in a slum? And who would benefit if you tried? Difficult questions about epidemiology's commitment to global health inequalities during Covid-19

The initial response to the Covid-19 pandemic was characterised by swift "lockdowns," a cluster of measures defined by a shared goal of suppressing Covid-19 and a shared character of restricting departure from the home except for specific purposes. By mid-April 2020, most countries were implementing stringent measures of this kind. This essay contends that (1) some epidemiologists played a central role in formulating and promulgating lockdown as a policy and (2) lockdowns were foreseeably harmful to the Global Poor, and foreseeably offered them little benefit, relative to less stringent measures. In view of the widespread commitment to reducing global health inequalities within the profession, this should prompt reflection within the epidemiological community and further work on pandemic response measures more appropriate for the Global Poor.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

The Use of Spatial Video to Map Dynamic and Challenging Environments: A Case Study of Cholera Risk in the Mujoga Relief Camp, D.R.C

In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method’s flexibility, we consider the life cycle of the Mujoga relief camp set up after the Nyiragongo volcanic eruption in the Democratic Republic of Congo on 22 May 2021. More specifically we investigate how these methods have captured the deteriorating conditions in a camp which is also experiencing lab-confirmed cholera cases. Spatial video data are collected every month from June 2021 to March 2022. These coordinate-tagged images are used to make monthly camp maps, which are then returned to the field teams for added contextual insights. At the same time, a zoom-based geonarrative is used to discuss the camp’s changes, including the cessation of free water supplies and the visible deterioration of toilet facilities. The paper concludes by highlighting the next data science advances to be made with SV mapping, including machine learning to automatically identify and map risks, and how these are already being applied in Mujoga.

Knowledge, attitude, and practice of Bangladeshi urban slum dwellers towards COVID-19 transmission-prevention: A cross-sectional study

The first COVID-19 case in Bangladesh was detected on March 8, 2020. Since then, efforts are being made across the country to raise awareness among the population for preventing the spread of this virus. We aimed to examine the urban slum dwellers' knowledge, attitude, and practice (KAP) towards COVID-19 transmission-prevention. A phone-based cross-sectional survey was conducted in five slums of Dhaka City. Total 476 adult slum dwellers were interviewed between October 31 to December 1, 2020 using a pre-tested questionnaire. During an interview, information was collected on participants' demographic characteristics and KAP items towards COVID-19. We used quartiles for categorization of knowledge and practice score where the first quartile represents poor, the second and third quartiles represent average while the fourth quartile represents good. Attitude score was standardized using z-score and identified as positive and negative attitude. Multiple linear regression models were used separately to identify the socioeconomic predictors of the KAP scores. The results showed that 25% of the respondents had good knowledge and 25% had poor knowledge, 48% had a positive attitude and 52% had a negative attitude, and 21% maintained good practice and 33% maintained poor practice towards COVID-19 transmission-prevention. About 75% respondents relied on television for COVID-19 related information. Regression results showed that knowledge and attitude scores were significantly higher if respondents had primary or secondary and above level of education compared to the uneducated group. Female respondents maintained significantly good practice compared to their male counterparts (β = 6.841; p<0.01). This study has found that one third of the studied slum dwellers maintained poor practice and one fourth had poor knowledge towards COVID-19 transmission-prevention. As KAP domains are significantly correlated, efforts are needed to raise awareness of COVID-19 particularly targeting individuals with average and lower knowledge to improve attitude and practice for the prevention of COVID-19.

Spatio-temporal distribution pattern of COVID-19 in the Northern Italy during the first-wave scenario: The role of the highway network

BACKGROUND

The rapid outbreak of Coronavirus disease 2019 (COVID-19) has posed several challenges to the scientific community. The goal of this paper is to investigate the spread of COVID-19 in Northern Italy during the so-called first wave scenario and to provide a qualitative comparison with the local highway net.

METHODS

Fixed a grid of days from February 27, 2020, the cumulative numbers of infections in each considered province have been compared to sequences of thresholds. As a consequence, a time-evolving classification of the state of danger in terms of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, in view of the smallest threshold overtaken by this comparison, has been obtained for each considered province. The provinces with a significant amount of cases have then been collected into matrices containing only the ones featuring a significant amount of cases.

RESULTS

The time evolution of the classification has then been qualitatively compared to the highway network, to identify similarities and thus linking the rapid spreading of COVID-19 and the highway connections.

CONCLUSIONS

The obtained results demonstrate how the proposed model properly fits with the spread of COVID-19 along with the Italian highway transport network and could be implemented to analyze qualitatively other disease transmissions in different contexts and time periods.

Are Kenyans Likely to Use COVID-19 Self-Testing Kits? Results From a Cross-Sectional Survey

Objectives: To understand the public’s perceptions around rapid SARS-CoV-2 antigen self-testing in Kenya, including the drivers of acceptability, willingness to pay, and adherence to hygiene and prevention recommendations following a positive self-test.

Methods: A household-based, cross-sectional survey, using a 35-item questionnaire, was conducted in Mombasa and Taita–Taveta counties, Kenya, during August 2021. Individuals aged ≥18 years were enrolled using a stratified sampling approach.

Results: There were 419 participants (mean age 35.7 years). A minority (10.5%) had ever tested for SARS-CoV-2. If SARS-CoV-2 self-testing were available, 39.9% and 41.5% would be likely and very likely, respectively, to use it. If unavailable free-of-charge, 63.01% would pay for it. Multivariate analyses suggested that people in rural areas (Coefficient 0.30, 95%CI: 0.11–0.48, p = 0.002), aged 36–55 (Coefficient 0.21, 95%CI: 0.03–0.40, p = 0.023), and employed full time (Coefficient 0.32, 95%CI: 0.06–0.58, p = 0.016) would have more odds to adhere to recommended hygiene and prevention actions.

Conclusion: SARS-CoV-2 self-testing was considered acceptable. Availability of self-testing could expand access to COVID-19 testing in Kenya, particularly among rural communities who have limited access to testing, and among mildly symptomatic individuals.

Health protection challenges of slums residents during the COVID-19 pandemic, according to the social determinants of health framework: A case study of Kerman city in Iran

BACKGROUND

Coronavirus disease-2019 (COVID-19) pandemic can aggravate the health problems in slum areas. The present study intends to examine the challenges of health protection of slum's residences during the COVID-19 pandemic with the Social Determinants of Health perspective, in Kerman city in Iran.

MATERIALS AND METHODS

The present study was a qualitative content analysis. Data were collected by purposeful sampling in-depth and semi-structured interviews with 16 people. The interviews were conducted (from October to December 2020) with the local representatives of the slum's residents, health-care workers, managers of facilitation offices, and welfare social service centers that are located in slum areas (Interview's guide is attached). The textual material from the interviews was entered into the MAXQDA software and directed content analysis was used to analyze the data.

RESULTS

Data analysis led to the identification of 4 categories (Sociocultural, Situational, Economical, and Physical environment) and 12 subcategories: Sociocultural: lack of awareness, begging culture, low trust in disease control programs, low bottom-up planning, and increasing violence; Situational: decreasing donors' activities and canceling important meetings; Economical: unemployment, decreasing in income, and increasing limitations of many institutions in providing resources; and Physical environment: inadequate space and inappropriate conditions in some houses and alleys for people protection against Corona viruses.

CONCLUSIONS

Slum residents have faced many challenges during the COVID-19 pandemic. These challenges can affect the health of slum and other urban dwellers. Multidisciplinary thinking and actions are needed. Increase awareness and engagement slum residents in control disease programs should be considered.

Cellular automata in the light of COVID-19

Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability (q) which helps us to measure the infectivity of a disease and (ii) exponent (n) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction.

What next? Expanding our view of city planning and global health, and implementing and monitoring evidence-informed policy

This Series on urban design, transport, and health aimed to facilitate development of a global system of health-related policy and spatial indicators to assess achievements and deficiencies in urban and transport policies and features. This final paper in the Series summarises key findings, considers what to do next, and outlines urgent key actions. Our study of 25 cities in 19 countries found that, despite many well intentioned policies, few cities had measurable standards and policy targets to achieve healthy and sustainable cities. Available standards and targets were often insufficient to promote health and wellbeing, and health-supportive urban design and transport features were often inadequate or inequitably distributed. City planning decisions affect human and planetary health and amplify city vulnerabilities, as the COVID-19 pandemic has highlighted. Hence, we offer an expanded framework of pathways through which city planning affects health, incorporating 11 integrated urban system policies and 11 integrated urban and transport interventions addressing current and emerging issues. Our call to action recommends widespread uptake and further development of our methods and open-source tools to create upstream policy and spatial indicators to benchmark and track progress; unmask spatial inequities; inform interventions and investments; and accelerate transitions to net zero, healthy, and sustainable cities.

The multidimensionality of anxiety among nursing students during COVID-19 pandemic: A cross-sectional study

BACKGROUND

Over the past year, healthcare workers constantly report their COVID-19 anxiety. However, this concept remained understudied among nursing students (NSs).

AIM

This study investigated the difference between NSs' three types of anxiety and their profile variables during the COVID-19 pandemic.

METHODS

This study used a cross-sectional, descriptive-correlational design. Three instruments were used: COVID-19 anxiety scale (CAS), COVID-19 anxiety syndrome scale (COVID-19ASS), and short health anxiety inventory (SHAI) to collect data from 484 Saudi NSs. We applied the Mann-Whitney U test and linear regression to analyze the data.

RESULTS

Across the three instruments; CAS, Item 1 "I feel bad when thinking about COVID-19"; COVID-19ASS, Item 11 "I have imagined what could happen to my family members if they contracted COVID-19"; and SHAI, Item 17 "A serious illness could ruin many aspects of my life" yielded the highest means. COVID-19ASS showed a significant difference for the profiles "known positive" (p = 0.05) and "action taken after with testing" (p = 0.05). NS, who knew someone with COVID-19, was the only predictor of CAS.

CONCLUSION

Our study concludes NSs experience anxiety symptoms. Anxiety is specific to COVID-19 or a set of similar anxiety symptoms. Further research is needed to explore the anxiety state of NSs during the pandemic.

Managing risk, governmentality and geoinformation: Vectors of vulnerability in the mapping of COVID‐19

In the wake of the COVID‐19 pandemic, a range of technological as well as legislative measures were introduced to monitor, track and prevent the spread of the COVID‐19 virus across the world. The measures taken by governments across the world have relied upon the use of geoinformation from satellites, drones, online dashboards and contact tracing apps to render the virus more visible, which has been instrumental in two ways. First, geoinformation has been helpful in organizing efforts for capacity building, in mapping communities living in deprived urban areas (referred to commonly as ‘slums’) and their response to COVID‐19 measures. These efforts have been part of initiatives by the United Nations as well as NGOs, using geoinformation to inform urban policymaking by representing the social, political and environmental issues facing those living in deprived urban areas. And secondly, geoinformation has also been used to control the spread of the pandemic by monitoring and limiting the behaviour of citizens through various technologies. This form of geoinformation‐driven governmentality, I will contend from critical geography and surveillance studies perspective endangers ethical values such as trust and solidarity, agency, transparency along with the rights and values of citizens.

Which urban communities are susceptible to COVID-19? An empirical study through the lens of community resilience

Background

After the lockdown of Wuhan on January 23, 2020, the government used community-based pandemic prevention and control as the core strategy to fight the pandemic, and explored a set of standardized community pandemic prevention measures that were uniformly implemented throughout the city. One month later, the city announced its first lists of “high-risk” communities and COVID-19-free communities. Under the standardized measures of pandemic prevention and mitigation, why some communities showed a high degree of resilience and effectively avoided escalation, while the situation spun out of control in other communities? This study investigated: 1) key factors that affect the effective response of urban communities to the pandemic, and 2) types of COVID-19 susceptible communities.

Methods

This study employs the crisp-set qualitative comparative analysis method to explore the influencing variables and possible causal condition combination paths that affect community resilience during the pandemic outbreak. Relying on extreme-case approach, 26 high-risk communities and 14 COVID-19 free communities were selected as empirical research subjects from the lists announced by Wuhan government. The community resilience assessment framework that evaluates the communities’ capacity on pandemic prevention and mitigation covers four dimensions, namely spatial resilience, capital resilience, social resilience, and governance resilience, each dimension is measured by one to three variables.

Results

The results of measuring the necessity of 7 single-condition variables found that the consistency index of “whether the physical structure of the community is favorable to virus transmission” reached 0.9, which constitutes a necessary condition for COVID-19 susceptible communities. By analyzing the seven condition configurations with high row coverage and unique coverage in the obtained complex solutions and intermediate solutions, we found that outbreaks are most likely to occur in communities populated by disadvantaged populations. However, if lacking spatial-, capital-, and governance resilience, middle-class and even wealthy communities could also become areas where COVID-19 spreads easily.

Conclusions

Three types of communities namely vulnerable communities, alienated communities, and inefficient communities have lower risk resilience. Spatial resilience, rather than social resilience, constitutes the key influencing factor of COVID-19-susceptible communities, and the dual deficiencies of social resilience and governance resilience are the common features of these communities.

Exploring the Ethics of Space in Slum Research during COVID-19 through the Lens of Merleau-Ponty

COVID-19 modifies a number of social behaviors and standards that we have been following. In slum research, the multifarious issues posed by COVID-19 are not limited to the increased disadvantages of slum inhabitants, but also to the closure of slums as a physical space conducive to understanding the slum dwellers’ plight. Their voices are silenced at a time when their narratives are critical for developing policies and initiatives to address their predicament. In this regard, the article will examine Merleau-Ponty’s concept of space and then utilize it to create an ethics of space, which is crucial in establishing the viability of virtual space as a creative space that may be used in lieu of traditional slum research. Finally, I discuss the advantages of using virtual space as an alternate space in slum research and how the study might be expanded in the post-COVID era.

Development of Econophysics: A Biased Account and Perspective from Kolkata

We present here a somewhat personalized account of the emergence of econophysics as an attractive research topic in physical, as well as social, sciences. After a rather detailed storytelling about our endeavors from Kolkata, we give a brief description of the main research achievements in a simple and non-technical language. We also briefly present, in technical language, a piece of our recent research result. We conclude our paper with a brief perspective.