Spatially resolved simulations of the spread of COVID-19 in European countries

We explore the spatial and temporal spread of the novel SARS-CoV-2 virus under containment measures in three European countries based on fits to data of the early outbreak. Using data from Spain and Italy, we estimate an age dependent infection fatality ratio for SARS-CoV-2, as well as risks of hospitalization and intensive care admission. We use them in a model that simulates the dynamics of the virus using an age structured, spatially detailed agent based approach, that explicitly incorporates governamental interventions, changes in mobility and contact patterns occurred during the COVID-19 outbreak in each country. Our simulations reproduce several of the features of its spatio-temporal spread in the three countries studied. They show that containment measures combined with high density are responsible for the containment of cases within densely populated areas, and that spread to less densely populated areas occurred during the late stages of the first wave. The capability to reproduce observed features of the spatio-temporal dynamics of SARS-CoV-2 makes this model a potential candidate for forecasting the dynamics of SARS-CoV-2 in other settings, and we recommend its application in low and lower-middle countries which remain understudied.

Forecasting the scale of the COVID-19 epidemic in Kenya

BackgroundThe first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya. MethodsWe developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak. ResultsWe find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 -2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

Modelling waste generated during construction of buildings using regression analysis.

The building industry is responsible for a large amount of waste, and the measurement and modelling of this waste could be used to develop better waste management plans. Several theoretical models explain the relationships between waste and building characteristics, but local practices may result in different behaviours. This study aimed to measure and analyse the waste generated through construction. It was based on the analysis of 18 building sites located in the region of Porto Alegre, Brazil. Waste was measured at these sites, and the results showed an average waste generation rate of 0.151 m3 m-2. A regression analysis of the collected data presented a satisfactory performance in two models. The first model was developed to explain total waste generation, including the effects of certain attributes, with an R2 = 0.81. The changes in waste generated during construction were estimated. The second model considered time schedules and examined the effect of the construction stage on waste generation, and reached an R2 = 0.91. The model with time indicated an S-shaped relationship. The models presented satisfactory statistical parameters and could be used to produce better waste management plans in the preconstruction stage.

Factors influencing temporary wood waste generation in high-rise building construction.

In Brazil, the use of wood in construction is primarily temporary, and it can represent a great percentage of construction waste. It is typically discarded with minimal reuse or recycling. As landfill wood disposal could result in methane emissions and/or leaching of hazardous constituents polluting water or soil, the implementation of temporary wood waste reduction strategies must be a critical issue for local construction companies. To manage and control wood waste generation, including setting some reduction goals, it is necessary to identify the influencing factors and ways to quantitatively predict their relative contributions. This study uses a multiple regression statistical model to estimate the amount of temporary wood waste generated in the construction of high-rise buildings by considering the influencing factors related to the design/construction as well as site and safety installations. The case study includes 22 high-rise residential buildings. The regression model predicted approximately 89% of the factors involved in the generation of wood waste in similar constructions. The dependent variables that had an influence on the amount of wood waste are related to design features ('number of floors'), structural construction system ('in situ moulded concrete volume'), and site and safety installations ('site wood fence' and 'wood use rate'). Ways of minimising wood waste generation and the contributions of each type of temporary wood use were discussed.