Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

Using Wastewater Surveillance to Compare COVID-19 Outbreaks during the Easter Holidays over a 2-Year Period in Cape Town, South Africa

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shown to be an important approach to determine early outbreaks of infections. Wastewater-based epidemiology (WBE) is regarded as a complementary tool for monitoring SARS-CoV-2 trends in communities. In this study, the changes in the SARS-CoV-2 RNA levels in wastewater during Easter holidays in 2021 and 2022 in the City of Cape Town were monitored over nine weeks. Our findings showed a statistically significant difference in the SARS-CoV-2 RNA viral load between the study weeks over the Easter period in 2021 and 2022, except for study week 1 and 4. During the Easter week, 52% of the wastewater treatment plants moved from the lower (low viral RNA) category in 2021 to the higher (medium to very high viral RNA) categories in 2022. As a result, the median SARS-CoV-2 viral loads where higher during the Easter week in 2022 than Easter week in 2021 (p = 0.0052). Mixed-effects model showed an association between the SARS-CoV-2 RNA viral loads and Easter week over the Easter period in 2021 only (p < 0.01). The study highlights the potential of WBE to track outbreaks during the holiday period.

The emission and dynamics of droplets from human expiratory activities and COVID-19 transmission in public transport system: A review

The public transport system, containing a large number of passengers in enclosed and confined spaces, provides suitable conditions for the spread of respiratory diseases. Understanding how diseases are transmitted in public transport environment is of vital importance to public health. However, this is a highly multidisciplinary matter and the related physical processes including the emissions of respiratory droplets, the droplet dynamics and transport pathways, and subsequently, the infection risk in public transport, are poorly understood. To better grasp the complex processes involved, a synthesis of current knowledge is required. Therefore, we conducted a review on the behaviors of respiratory droplets in public transport system, covering a wide scope from the emission profiles of expiratory droplets, the droplet dynamics and transport, to the transmission of COVID-19 in public transport. The literature was searched using related keywords in Web of Science and PubMed and screened for suitability. The droplet size is a key parameter in determining the deposition and evaporation, which together with the exhaled air velocity largely determines the horizontal travel distance. The potential transmission route and transmission rate in public transport as well as the factors influencing the virus-laden droplet behaviors and virus viability (such as ventilation system, wearing personal protective equipment, air temperature and relative humidity) were also discussed. The review also suggests that future studies should address the uncertainties in droplet emission profiles associated with the measurement techniques, and preferably build a database based on a unified testing protocol. Further investigations based on field measurements and modeling studies into the influence of different ventilation systems on the transmission rate in public transport are also needed, which would provide scientific basis for controlling the transmission of diseases.

Modeling the effects of the governmental responses to COVID-19 on transit demand: The case of Athens, Greece

Short-term demand forecasting is essential for the public transit system, allowing for effective operations planning. This is especially relevant in the highly uncertain environment created by the SARS‑CoV‑2 pandemic. In this paper, we attempt to develop accurate prediction models of transit ridership in Athens, Greece, using Autoregressive Fractional Integrated time series models enhanced with SARS‑CoV‑2-related exogenous variables. The selected exogenous variables are, from the one hand, the ratio of weekly SARS‑CoV‑2 infections over the infections 3 weeks before (capturing the dynamics of the pandemic, as a proxy for fear of transmitting the disease while commuting), and from the other hand, an index of the stringency of the government's SARS‑CoV‑2-related measures and regulations. The developed ARFIMAX models have been fitted separately on bus and metro ridership data and wield comparable and statistically significant results. In both models, the exogenous variables prove to be statistically significant and their values are intuitive, suggesting a linear interrelation between them and transit ridership.

Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness

OBJECTIVES

The effective reproduction number (R_t) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely R_t data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting R_t in real time, using timely accessible data - i.e. human mobility, temperature, and risk awareness.

METHODS

A linear regression model to predict R_t was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures.

RESULTS

The predicted mean values of R_t and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when R_t changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented.

CONCLUSIONS

The described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.

Predicting COVID-19 incidence in French hospitals using human contact network analytics

Background  COVID-19 was first detected in Wuhan, China, in 2019 and spread worldwide within a few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Subnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. Methods  Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. We added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. Findings  We found that predictions can be improved substantially (by more than 50%) at both the national level and the subnational level for up to 2 weeks. Our subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.

Dynamics of the COVID-19 epidemic in Ireland under mitigation

BACKGROUND

In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination.

METHODS

To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data.

RESULTS

In Ireland, mitigation measures provided a 78-86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2-3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved.

CONCLUSION

We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.

First back-calculation and infection fatality multiplier estimate of the hidden prevalence of COVID-19 in Ireland

Background

To date computer models with multiple assumptions have focussed on predicting the incidence of symptomatic cases of COVID-19. Given emerging vaccines, the aim of this study was to provide simple methods for estimating the hidden prevalence of asymptomatic cases and levels of herd immunity to aid future immunization policy and planning. We applied the method in Ireland.

Methods

For large scale epidemics, indirect models for estimating prevalence have been developed. One such method is the benchmark multiplier method. A further method is back-calculation, which has been used successfully to produce estimates of the scale of a HIV infected population. The methods were applied from March to October 2020 and are applicable globally.

Results

Results demonstrated that the number of infected individuals was at least twice and possibly six times the number identified through testing. Our estimates ranged from ∼100 000 to 375 000 cases giving a ratio of 1–6 hidden cases for every known case within the study time frame. While both methods are subject to assumptions and limitations, it was interesting to observe that estimates corroborated government statements noting that 80% of people testing positive were asymptomatic.

Conclusions

As Europe has now endured several epidemic waves with the emergence globally of new variants, it essential that both policy makers and the public are aware of the scale of the hidden epidemic that may surround them. The need for social distancing is as important as ever as we await global immunization rollout.

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).