A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

A phenomenological neural network powered by the National Wastewater Surveillance System for estimation of silent COVID-19 infections

Although wastewater-based epidemiology (WBE) has emerged as an inexpensive and non-intrusive method in contrast to clinical testing to track public health at community levels, there is a lack of structured interpretative criteria to translate the SARS-CoV-2 concentrations in wastewater to COVID-19 infection cases. The difficulties lie in the uncertainties of the amount of virus shed by an infected individual to wastewater as documented in clinical studies. This situation is even worse considering the existence of a population of silent infections and many other confounding factors. In this research, a quantitative framework of a phenomenological neural network (PNN) was developed to compute silent infections. The PNN was trained using the WBE data from the National Wastewater Surveillance System (NWSS) - a program launched by the CDC of the United States in 2020. It is found that the PNN excelled with superior interpretability and reduced overfitting. A big-data perspective on virus shedding by an infected population revealed more deterministic virus-shedding dynamics compared to the clinical studies perspective on virus shedding by an infected individual. With such characteristics employed as the theoretical basis for the estimation of the silent infections, a ratio of silent to reported infections was found to be 5.7 as the national median during the studied period. The study also noted the influence of temperature, sewershed population, and per-capita flow rates on the computation of silent infections. It is expected that the proposed framework in this work would facilitate public health actions guided by the SARS-CoV-2 concentrations in wastewater. In case of a new wave emergence or a new virus disease outbreak like COVID-19, the PNN powered by the NWSS would outline consolidated and systematic information that would enable rapid deployment of public health actions.

A novel online multi-task learning for COVID-19 multi-output spatio-temporal prediction

In light of the ongoing COVID-19 pandemic, predicting its trend would significantly impact decision-making. However, this is not a straightforward task due to three main difficulties: temporal autocorrelation, spatial dependency, and concept drift caused by virus mutations and lockdown policies. Although machine learning has been extensively used in related work, no previous research has successfully addressed all three challenges simultaneously. To overcome this challenge, we developed a novel online multi-task regression algorithm that incorporates a chain structure to capture spatial dependency, the ADWIN drift detector to adapt to concept drift, and the lag time series feature to capture temporal autocorrelation. We conducted several comparative experiments based on the number of daily confirmed cases in 20 areas in California and affiliated cities. The results from our experiments demonstrate that our proposed model is superior in adapting to concept drift in COVID-19 data and capturing spatial dependencies across various regions. This leads to a significant improvement in prediction accuracy when compared to existing state-of-the-art batch machine learning methods, such as N-Beats, DeepAR, TCN, and LSTM.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m² showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Substantial impact of mobility restrictions on reducing COVID-19 incidence in Italy in 2020

BACKGROUND

Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light and the second one tight. By analyzing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy.

METHODS

We measured individual mobility during the first epidemic wave with mobile phone movements tracked through carrier networks, and related this mobility to daily new SARS-CoV-2 infections, hospital admissions, intensive care admissions and deaths attributed to COVID-19, taking into account reason for travel (work-related or not) and the means of transport.

RESULTS

The tight lockdown resulted in an 82% reduction in mobility for the entire country and was effective in swiftly curbing the outbreak as indicated by a shorter time-to-peak of all health outcomes, particularly for provinces with the highest mobility reductions and the most intense COVID-19 spread. Reduction of work-related mobility was accompanied by a nearly linear benefit in outbreak containment; work-unrelated movements had a similar effect only for restrictions exceeding 50%. Reduction in mobility by car and by airplane was nearly linearly associated with a decrease in most COVID-19 health outcomes, while for train travel reductions exceeding 55% had no additional beneficial effects. The absence of viral variants and vaccine availability during the study period eliminated confounding from these two sources.

CONCLUSIONS

Adherence to the COVID-19 tight lockdown during the first wave in Italy was high and effective in curtailing the outbreak. Any work-related mobility reduction was effective, but only high reductions in work-unrelated mobility restrictions were effective. For train travel, there was a threshold above which no further benefit occurred. These findings could be particular to the spread of SARS-CoV-2, but might also apply to other communicable infections with comparable transmission dynamics.