Crowding has consequences: Prevention and management of COVID-19 in informal urban settlements

Seidlein, Lorenz von; Alabaster, Graham; Deen, Jacqueline; Knudsen, Jakob

doi:10.1016/j.buildenv.2020.107472

Cited by 90 publications

(79 citation statements)

References 30 publications

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“…The number of reported cases as well as the CFR depend on the intensity of testing and the characteristics of populations tested in respective countries. The very low CFR in Singapore ( Table 2 ) is thus explained not only by the effective containment program in this city state, but also by mass testing of the population of young (usually 20–30 years old) migrant workers, who were living in crowded dormitories where SARS-CoV-2 could spread easily [ 11 , 12 ]. In contrast, the high CFR in China is likely the result of a certain degree of underreporting, combined with an overwhelmed health system in Wuhan during the first epidemic wave.…”

Section: Discussionmentioning

confidence: 99%

“…While CFR estimates are usually higher compared to IFRs as they are simply calculated from the number of positive tests without consideration of the true denominator, IFR estimates are producing a more realistic picture based on available data from closed cohort studies and serological surveys; they range from 0.2% to 0.4% globally and from 0.4 to 1% in Germany [8,9]. SARS-CoV-2 is mainly transmitted through droplets and aerosols during conversations, shouting, singing, and exercising, and where people congregate in poorly ventilated indoor places; transmission through fomites seems to play a minor role [10][11][12]. The virus initially replicates in the upper respiratory tract and COVID-19 cases already become infectious the days before symptoms occur; this is a critical difference to the epidemiological characteristics of SARS-CoV-1 in which infected persons start infecting others only after the onset of symptoms; this has major implications regarding the success of control measures [13].…”

Section: Introductionmentioning

confidence: 99%

“…Health workers are now better protected compared to the beginning of the pandemic but remain a major risk group [7]. Today, most transmission occurs where unprotected people stay closely together for prolonged periods of time, as it is primarily the case in households and during informal gatherings of relatives and friends [11][12][13][14]. Transmission risk is also high in crowded places such as dormitories, nursing homes, prisons, refugee camps, cruise ships, and certain work places [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Today, most transmission occurs where unprotected people stay closely together for prolonged periods of time, as it is primarily the case in households and during informal gatherings of relatives and friends [11][12][13][14]. Transmission risk is also high in crowded places such as dormitories, nursing homes, prisons, refugee camps, cruise ships, and certain work places [11,12]. Superspreading, which is associated with large public or private gatherings (e.g.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 in Germany and China: mitigation versus elimination strategy

Razum

Jahn

et al. 2021

Global Health Action

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Background : The COVID-19 pandemic shows variable dynamics in WHO Regions, with lowest disease burden in the Western-Pacific Region. While China has been able to rapidly eliminate transmission of SARS-CoV-2, Germany – as well as most of Europe and the Americas – is struggling with high numbers of cases and deaths. Objective : We analyse COVID-19 epidemiology and control strategies in China and in Germany, two countries which have chosen profoundly different approaches to deal with the epidemic. Methods : In this narrative review, we searched the literature from 1 December 2019, to 4 December 2020. Results : China and several neighbours (e.g. Australia, Japan, South Korea, New Zealand, Thailand) have achieved COVID-19 elimination or sustained low case numbers. This can be attributed to: (1) experience with previous coronavirus outbreaks; (2) classification of SARS-CoV-2 in the highest risk category and consequent early employment of aggressive control measures; (3) mandatory isolation of cases and contacts in institutions; (4) broad employment of modern contact tracking technology; (5) travel restrictions to prevent SARS-CoV-2 re-importation; (6) cohesive communities with varying levels of social control. Conclusions : Early implementation of intense and sustained control measures is key to achieving a near normal social and economic life.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COVID-19 in Germany and China: mitigation versus elimination strategy

Razum

Jahn

et al. 2021

Global Health Action

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“…The principal idea of the proposed framework is the high correlation between the public mobility estimate and potential increase of SARS-CoV-2 infections. Crowded regions would lead to high probabilities of viral transmission [ 38 , 39 ]. Moreover, we believe that changes in meteorological factors have some useful insights that may improve forecasting results.…”

Section: Materials and Methodsmentioning

confidence: 99%

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Rashed

Hirata

2021

IJERPH

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With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.

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Spatial assessment on influence of land use and population density in the achievement score of sustainable development target 11.1

Baskaran¹,

Velkennedy²

2023

Sustainable Development

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The United Nations (U.N.) has declared the present decade a “decade of action” to accelerate the phase of 17 Sustainable Development Goals (SDGs) endorsed in 2015. These 17 goals are intended to protect the earth's environment, eradicate poverty and inequality, and guarantee that everyone lives in peace, harmony, and prosperity. Target 11.1 for safe and affordable housing from SDG 11: Sustainable cities and community is a crucial target for most developing nations. This target addresses the slums and informal settlements, which indicate inequalities in the urbanized city. The proportion of the population living in slum dwellings and informal settlements was the indicator to measure the achievement level of the target. The study area to assess the target is Tirunelveli City municipal corporation from southern India. The field survey was conducted in all 55 wards to locate and count the population of slums and informal settlements and to map the spatial variation of target scores among the wards. The spatial assessment was performed by comparing the attributes of population density and land use classes (water, built area, and bare land) with the achievement score of target 11.1 for each ward. With an overall score of 90.08, this city has no significant relation with population density (R2 = 0.07) and relevant land use classes (R2 = 0.05) at the ward level. The spatial tool, weighted mean centre, has concluded that slum settlements appeared randomly and dispersed throughout Tirunelveli without any pattern.

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Crowding has consequences: Prevention and management of COVID-19 in informal urban settlements

Cited by 90 publications

References 30 publications

COVID-19 in Germany and China: mitigation versus elimination strategy

COVID-19 in Germany and China: mitigation versus elimination strategy

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

Spatial assessment on influence of land use and population density in the achievement score of sustainable development target 11.1

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