Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

Evaluating the association between COVID-19 transmission and mobility in omicron outbreaks in China

BACKGROUND

Prior research has suggested a positive correlation between human mobility and COVID-19 transmission at national or provincial levels, assuming constant correlations during outbreaks. However, the correlation strength at finer scales and potential changes in relationships during outbreaks have been scarcely investigated.

METHODS

We gathered case and mobility data (within-city movement, inter-city inflow, and inter-city outflow) at the city level from Omicron outbreaks in mainland China between February and November 2022. For each outbreak, we calculated the time-varying effective reproduction number (Rt). Subsequently, we estimated the cross-correlation and rolling correlation between Rt and the mobility index, comparing them and identifying potential factors affecting these correlations.

RESULTS

We identify 57 outbreaks during Omicron wave 1 (February to June) and 171 outbreaks during Omicron wave 2 (July to December). Cross-correlation estimates vary between waves, with values ranging from 0.64 to 0.71 in wave 1 and 0.45 to 0.46 in wave 2. Oscillation models best fit the rolling correlation for almost all outbreaks, and there are significant differences between extreme values of rolling correlation and cross-correlation. Additionally, we estimate a positive relationship between the GRI and rolling correlation during the pre-peak stage, turning negative during the post-peak stage.

CONCLUSIONS

Our findings suggest a positive relationship between Omicron transmission and mobility at the city level. However, significant fluctuations in their relationship, as demonstrated by rolling correlation, indicate that assuming a constant correlation between transmission and mobility may lead to inaccurate predictions or decisions when using mobility as a proxy for transmission intensity.

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong

The serial interval (SI) distribution of an epidemic is used to approximate the generation time distribution, an essential parameter for inferring the transmissibility (${R}_t$) of an infectious disease. However, SI distributions may change as an epidemic progresses. We examined detailed contact tracing data on laboratory-confirmed cases of COVID-19 in Hong Kong, China, during the 5 COVID-19 waves from January 2020 to July 2022. We reconstructed the transmission pairs and estimated time-varying effective SI distributions and factors associated with longer or shorter intervals. Finally, we assessed the biases in estimating transmissibility using constant SI distributions. We found clear temporal changes in mean SI estimates within each epidemic wave studied and across waves, with mean SIs ranging from 5.5 days (95% credible interval, 4.4-6.6) to 2.7 days (95% credible interval, 2.2-3.2). The mean SIs shortened or lengthened over time, which was found to be closely associated with the temporal variation in COVID-19 case profiles and public health and social measures and could lead to biases in predicting ${R}_t$. Accounting for the impact of these factors, the time-varying quantification of SI distributions could lead to improved estimation of ${R}_t$, and could provide additional insights into the impact of public health measures on transmission.

Pilot study: a descriptive-retrospective analysis of SARS-CoV-2 variants distribution and phylogenesis in the Phlegraean area

COVID-19 disease, caused by SARS-CoV-2 virus, marked the pandemic era, opening the way to next-generation sequencing in the viral diagnostic field. SARS-CoV-2 viral genome sequencing makes it possible to identify mutations in the virus and to track the diffusion of these variants in specific geographic area and in time. Variant sequences help understand how the virus spreads and how it can be contained, as well as for developing more effective vaccines and therapies. Indeed, monitoring the evolution of a virus allows us to quickly detect the potential selection of a super mutation, which can make a virus even more contagious and dangerous in terms of human health consequences. In light of this, in our pilot study, we decided to profile the SARS-CoV-2 genome, recruiting 38 patients divided according to age, sex, vaccination status and symptoms, ascertaining their positivity to the virus. Specific strains of SARS-CoV-2 have been identified and effective through next-generation sequencing. This analysis made it possible to obtain information on the variants of the virus and their spread in the Campania region of the Phlegraean area, in the municipalities of Bacoli, Pozzuoli and Monte di Procida from December 2021 to February 2023 and on the effect of long-term measures COVID-19 in our sample. The advantage of using NGS in diagnosis is the introduction of tests on many genes in a relatively short time and at relatively low costs, with a consequent increase in a precise molecular diagnosis and helps to identify ad personam therapies.

A systematic review of environmental covariates and methods for spatial or temporal scrub typhus distribution prediction

BACKGROUND

Scrub typhus is underdiagnosed and underreported but emerging as a global public health problem. To inform future burden and prediction studies we examined through a systematic review the potential effect of environmental covariates on scrub typhus occurrence and the methods which have been used for its prediction.

METHODS

In this systematic review, we searched PubMed, Scopus, Web of Science, China National Knowledge Infrastructure and other databases, with no language and publication time restrictions, for studies that investigated environmental covariates or utilized methods to predict the spatial or temporal human. Data were manually extracted following a set of queries and systematic analysis was conducted.

RESULTS

We included 68 articles published in 1978-2024 with relevant data from 7 countries/regions. Significant environmental risk factors for scrub typhus include temperature (showing positive or inverted-U relationships), precipitation (with positive or inverted-U patterns), humidity (exhibiting complex positive, inverted-U, or W-shaped associations), sunshine duration (with positive, inverted-U associations), elevation, the normalized difference vegetation index (NDVI), and the proportion of cropland. Socioeconomic and biological factors were rarely explored. Autoregressive Integrated Moving Average (ARIMA) (n = 8) and ecological niche modelling (ENM) approach (n = 11) were the most popular methods for predicting temporal trends and spatial distribution of scrub typhus, respectively.

CONCLUSIONS

Our findings summarized the evidence on environmental covariates affecting scrub typhus occurrence and the methodologies used for predictive modelling. We review the existing knowledge gaps and outline recommendations for future studies modelling disease prediction and burden.

TRIAL REGISTRATION

PROSPERO CRD42022315209.

A self-driven ESN-DSS approach for effective COVID-19 time series prediction and modelling

Since the outbreak of the COVID-19 epidemic, it has posed a great crisis to the health and economy of the world. The objective is to provide a simple deep-learning approach for predicting, modelling, and evaluating the time evolutions of the COVID-19 epidemic. The Dove Swarm Search (DSS) algorithm is integrated with the echo state network (ESN) to optimize the weight. The ESN-DSS model is constructed to predict the evolution of the COVID-19 time series. Specifically, the self-driven ESN-DSS is created to form a closed feedback loop by replacing the input with the output. The prediction results, which involve COVID-19 temporal evolutions of multiple countries worldwide, indicate the excellent prediction performances of our model compared with several artificial intelligence prediction methods from the literature (e.g., recurrent neural network, long short-term memory, gated recurrent units, variational auto encoder) at the same time scale. Moreover, the model parameters of the self-driven ESN-DSS are determined which acts as a significant impact on the prediction performance. As a result, the network parameters are adjusted to improve the prediction accuracy. The prediction results can be used as proposals to help governments and medical institutions formulate pertinent precautionary measures to prevent further spread. In addition, this study is not only limited to COVID-19 time series forecasting but also applicable to other nonlinear time series prediction problems.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Transmission Patterns of Co-Circulation of Omicron Sub-Lineages in Hong Kong SAR, China, a City with Rigorous Social Distancing Measures, in 2022

OBJECTIVE

This study aimed to characterize the changing landscape of circulating SARS-CoV-2 lineages in the local community of Hong Kong throughout 2022. We examined how adjustments to quarantine arrangements influenced the transmission pattern of Omicron variants in a city with relatively rigorous social distancing measures at that time.

METHODS

In 2022, a total of 4684 local SARS-CoV-2 genomes were sequenced using the Oxford Nanopore GridION sequencer. SARS-CoV-2 consensus genomes were generated by MAFFT, and the maximum likelihood phylogeny of these genomes was determined using IQ-TREE. The dynamic changes in lineages were depicted in a time tree created by Nextstrain. Statistical analysis was conducted to assess the correlation between changes in the number of lineages and adjustments to quarantine arrangements.

RESULTS

By the end of 2022, a total of 83 SARS-CoV-2 lineages were identified in the community. The increase in the number of new lineages was significantly associated with the relaxation of quarantine arrangements (One-way ANOVA, F(5, 47) = 18.233, p < 0.001)). Over time, Omicron BA.5 sub-lineages replaced BA.2.2 and became the predominant Omicron variants in Hong Kong. The influx of new lineages reshaped the dynamics of Omicron variants in the community without fluctuating the death rate and hospitalization rate (One-way ANOVA, F(5, 47) = 2.037, p = 0.091).

CONCLUSION

This study revealed that even with an extended mandatory quarantine period for incoming travelers, it may not be feasible to completely prevent the introduction and subsequent community spread of highly contagious Omicron variants. Ongoing molecular surveillance of COVID-19 remains essential to monitor the emergence of new recombinant variants.

Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic

Background

The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate.

Methods

We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number.

Results

Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (β = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models.

Discussion

Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation

Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.

Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates

The time-varying effective reproduction number Rt is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of Rt can be obtained from reported cases counted by their date of symptom onset, which is generally closer to the time of infection than the date of report. Case counts by date of symptom onset are typically obtained from line list data, however these data can have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and Rt estimation into a single generative Bayesian model, allowing direct joint inference of case counts and Rt from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different epidemic phases. We find that under reporting delays realistic for hospitalization data (50% of reports delayed by more than a week), intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and Rt, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. The generative modeling components developed in this work have been integrated and further extended in the R package epinowcast, providing a flexible and interpretable tool for real-time surveillance.

Comparison of control and transmission of COVID-19 across epidemic waves in Hong Kong: an observational study

BACKGROUND

Hong Kong contained COVID-19 for two years but experienced a large epidemic of Omicron BA.2 in early 2022 and endemic transmission of Omicron subvariants thereafter. We reflected on pandemic preparedness and responses by assessing COVID-19 transmission and associated disease burden in the context of implementation of various public health and social measures (PHSMs).

METHODS

We examined the use and impact of pandemic controls in Hong Kong by analysing data on more than 1.7 million confirmed COVID-19 cases and characterizing the temporal changes non-pharmaceutical and pharmaceutical interventions implemented from January 2020 through to 30 December 2022. We estimated the daily effective reproductive number (Rt) to track changes in transmissibility and effectiveness of community-based measures against infection over time. We examined the temporal changes of pharmaceutical interventions, mortality rate and case-fatality risks (CFRs), particularly among older adults.

FINDINGS

Hong Kong experienced four local epidemic waves predominated by the ancestral strain in 2020 and early 2021 and prevented multiple SARS-CoV-2 variants from spreading in the community before 2022. Strict travel-related, case-based, and community-based measures were increasingly tightened in Hong Kong over the first two years of the pandemic. However, even very stringent measures were unable to contain the spread of Omicron BA.2 in Hong Kong. Despite high overall vaccination uptake (>70% with at least two doses), high mortality was observed during the Omicron BA.2 wave due to lower vaccine coverage (42%) among adults ≥65 years of age. Increases in antiviral usage and vaccination uptake over time through 2022 was associated with decreased case fatality risks.

INTERPRETATION

Integrated strict measures were able to reduce importation risks and interrupt local transmission to contain COVID-19 transmission and disease burden while awaiting vaccine development and rollout. Increasing coverage of pharmaceutical interventions among high-risk groups reduced infection-related mortality and mitigated the adverse health impact of the pandemic.

FUNDING

Health and Medical Research Fund.

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Monitoring the effective reproduction number [Formula: see text] of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating [Formula: see text] at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases.

COVID-19 case rates, spatial mobility, and neighbourhood socioeconomic characteristics in Toronto: a spatial-temporal analysis

OBJECTIVES

This study has two primary research objectives: (1) to investigate the spatial clustering pattern of mobility reductions and COVID-19 cases in Toronto and their relationships with marginalized populations, and (2) to identify the most relevant socioeconomic characteristics that relate to human mobility and COVID-19 case rates in Toronto's neighbourhoods during five distinct time periods of the pandemic.

METHODS

Using a spatial-quantitative approach, we combined hot spot analyses, Pearson correlation analyses, and Wilcoxon two-sample tests to analyze datasets including COVID-19 cases, a mobile device-derived indicator measuring neighbourhood-level time away from home (i.e., mobility), and socioeconomic data from 2016 census and Ontario Marginalization Index. Temporal variations among pandemic phases were examined as well.

RESULTS

The paper identified important spatial clustering patterns of mobility reductions and COVID-19 cases in Toronto, as well as their relationships with marginalized populations. COVID-19 hot spots were in more materially deprived neighbourhood clusters that had more essential workers and people who spent more time away from home. While the spatial pattern of clusters of COVID-19 cases and mobility shifted slightly over time, the group socioeconomic characteristics that clusters shared remained similar in all but the first time period. A series of maps and visualizations were created to highlight the dynamic spatiotemporal patterns.

CONCLUSION

Toronto's neighbourhoods have experienced the COVID-19 pandemic in significantly different ways, with hot spots of COVID-19 cases occurring in more materially and racially marginalized communities that are less likely to reduce their mobility. The study provides solid evidence in a Canadian context to enhance policy making and provide a deeper understanding of the social determinants of health in Toronto during the COVID-19 pandemic.

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

Predicting onset risk of COVID-19 symptom to support healthy travel route planning in the new normal of long-term coexistence with SARS-CoV-2

Due to the increased outdoor transmission risk of new SARS-COV-2 variants, the health of urban residents in daily travel is being threatened. In the new normal of long-term coexistence with SARS-CoV-2, how to avoid being infected by SARS-CoV-2 in daily travel has become a key issue. Hence, a spatiotemporal solution has been proposed to assist healthy travel route planning. Firstly, an enhanced urban-community-scale geographic model was proposed to predict daily COVID-19 symptom onset risk by incorporating the real-time effective reproduction numbers, and daily population variation of fully vaccinated. On-road onset risk predictions in the next following days were then extracted for searching healthy routes with the least onset risk values. The healthy route planning was further implemented in a mobile application. Hong Kong, one of the representative highly populated cities, has been chosen as an example to apply the spatiotemporal solution. The application results in the four epidemic waves of Hong Kong show that based on the high accurate prediction of COVID-19 symptom onset risk, the healthy route planning could reduce people's exposure to the COVID-19 symptoms onset risk. To sum, the proposed solution can be applied to support the healthy travel of residents in more cities in the new normalcy.

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

The estimation of human mobility patterns is essential for many components of developed societies, including the planning and management of urbanization, pollution, and disease spread. One important type of mobility estimator is the next-place predictors, which use previous mobility observations to anticipate an individual's subsequent location. So far, such predictors have not yet made use of the latest advancements in artificial intelligence methods, such as General Purpose Transformers (GPT) and Graph Convolutional Networks (GCNs), which have already achieved outstanding results in image analysis and natural language processing. This study explores the use of GPT- and GCN-based models for next-place prediction. We developed the models based on more general time series forecasting architectures and evaluated them using two sparse datasets (based on check-ins) and one dense dataset (based on continuous GPS data). The experiments showed that GPT-based models slightly outperformed the GCN-based models with a difference in accuracy of 1.0 to 3.2 percentage points (p.p.). Furthermore, Flashback-LSTM-a state-of-the-art model specifically designed for next-place prediction on sparse datasets-slightly outperformed the GPT-based and GCN-based models on the sparse datasets (1.0 to 3.5 p.p. difference in accuracy). However, all three approaches performed similarly on the dense dataset. Given that future use cases will likely involve dense datasets provided by GPS-enabled, always-connected devices (e.g., smartphones), the slight advantage of Flashback on the sparse datasets may become increasingly irrelevant. Given that the performance of the relatively unexplored GPT- and GCN-based solutions was on par with state-of-the-art mobility prediction models, we see a significant potential for them to soon surpass today's state-of-the-art approaches.

A new method for the joint estimation of instantaneous reproductive number and serial interval during epidemics

Although some methods for estimating the instantaneous reproductive number during epidemics have been developed, the existing frameworks usually require information on the distribution of the serial interval and/or additional contact tracing data. However, in the case of outbreaks of emerging infectious diseases with an unknown natural history or undetermined characteristics, the serial interval and/or contact tracing data are often not available, resulting in inaccurate estimates for this quantity. In the present study, a new framework was specifically designed for joint estimates of the instantaneous reproductive number and serial interval. Concretely, a likelihood function for the two quantities was first introduced. Then, the instantaneous reproductive number and the serial interval were modeled parametrically as a function of time using the interpolation method and a known traditional distribution, respectively. Using the Bayesian information criterion and the Markov Chain Monte Carlo method, we ultimately obtained their estimates and distribution. The simulation study revealed that our estimates of the two quantities were consistent with the ground truth. Seven data sets of historical epidemics were considered and further verified the robust performance of our method. Therefore, to some extent, even if we know only the daily incidence, our method can accurately estimate the instantaneous reproductive number and serial interval to provide crucial information for policymakers to design appropriate prevention and control interventions during epidemics.

Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis

BACKGROUND

While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19.

OBJECTIVE

We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events.

METHODS

We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc.

RESULTS

A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%).

CONCLUSIONS

We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.

Depression risk among community-dwelling older people is associated with perceived COVID-19 infection risk: effects of news report latency and focusing on number of infected cases

Awareness of COVID-19 infection risk and oscillation patterns ('waves') may affect older people's mental health. Empirical data from populations experiencing multiple waves of community outbreaks can inform guidance for maintaining mental health. This study aims to investigate the effects of COVID-19 infection risk and oscillations on depression among community-dwelling older people in Hong Kong.

A rolling cross-sectional telephone survey method was used. Screening for depression risk was conducted among 8,163 older people (age ≥ 60) using the Patient Health Questionnaire-2 (PHQ-2) from February to August 2020. The relationships between PHQ-2, COVID-19 infection risk proxies - change in newly infected cases and effective reproductive number (R_t), and oscillations - stage of a 'wave' reported in the media, were analysed using correlation and regression.

8.4% of survey respondents screened positive for depression risk. Being female (β = .08), having a pre-existing mental health issue (β = .21), change in newly infected cases (β = .05), and screening during the latency period before the media called out new waves (β = .03), contributed to higher depression risk (R² = .06, all p <.01).

While depression risk does not appear alarming in this sample, our results highlight that older people are sensitive to reporting of infection, particularly among those with existing mental health needs. Future public health communication should balance awareness of infection risks with mental health protection.

Estimating the transmission dynamics of SARS-CoV-2 Omicron BF.7 in Beijing after adjustment of the zero-COVID policy in November-December 2022

We tracked the effective reproduction number (R_t) of the predominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron BF.7 in Beijing in November-December 2022 by fitting a transmission dynamic model parameterized with real-time mobility data to (i) the daily number of new symptomatic cases on 1-11 November (when China's zero-COVID interventions were still strictly enforced) and (ii) the proportion of individuals who participated in online polls on 10-22 December and self-reported to have been test-positive since 1 November. After China's announcement of 20 measures to transition from zero-COVID, we estimated that R_t increased to 3.44 (95% credible interval (CrI): 2.82-4.14) on 18 November and the infection incidence peaked on 11 December. We estimated that the cumulative infection attack rate (IAR; that is, proportion of the population infected since 1 November) in Beijing was 75.7% (95% CrI: 60.7-84.4) on 22 December 2022 and 92.3% (95% CrI: 91.4-93.1) on 31 January 2023. Surveillance programs should be rapidly set up to monitor the evolving epidemiology and evolution of SARS-CoV-2 across China.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

BACKGROUND

The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

METHODS

We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.

RESULTS

The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.

CONCLUSIONS

The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Negative ageism and compassionate ageism in news coverage of older people under COVID-19: how did the pandemic progression and public health responses associate with different news themes?

Previous studies have found negative ageing narratives in the media during the COVID-19 pandemic. However, few have focused on compassionate ageism and how the news responded to the progression of the COVID-19 pandemic. We investigated (a) media themes of negative and compassionate ageism and (b) their relationships with COVID-19 parameters and the public health response. The sample included 1,197 articles relevant to COVID-19 and older people in Hong Kong published between January and December 2020. We used thematic analysis to identify themes from the news articles and structural equation modelling to explore these themes' relationship with the number of older people infected, effective reproduction number, number of COVID-19 deaths and public health response parallel in time. Pandemic-related variables were lagged for a day – the time needed to be reflected in the news. Two negative ageism themes portrayed older people as vulnerable to COVID-19 but counterproductive in combating the pandemic. Two compassionate ageism themes depicted older people as a homogenous group of passive assistance recipients. The theme blaming older people was associated with the number of confirmed infections (β = 0.418, p = 0.002) but vulnerability of older people was not associated with pandemic-related variables. The theme helping older people was negatively associated with the percentage of older people in confirmed infections (β = −0.155, p = 0.019). The theme resources available was negatively associated with confirmed infections (β = −0.342, p < 0.001) but positively associated with the Containment and Health Index (β = 0.217, p = 0.005). Findings suggested that negative and compassionate ageism were translated into narratives about older people in the media as the pandemic evolved but did not address the actual risk they faced. Media professionals should be aware of the potential negative and compassionate ageism prompted by the news agenda and promote adequate health behaviours and responses.

A modelling approach to estimate the transmissibility of SARS-CoV-2 during periods of high, low, and zero case incidence

Against a backdrop of widespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major outbreaks, the effective reproduction number can be estimated from a time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanistic modelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 from periods of high to low - or zero - case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low - or zero - case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

BACKGROUND

Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the "global normalcy index" because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong.

OBJECTIVE

Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy.

METHODS

Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high-onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work.

RESULTS

Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high-onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs.

CONCLUSIONS

In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people's normal lives.

Digitalization impacts the COVID-19 pandemic and the stringency of government measures

COVID-19 poses a significant burden to populations worldwide. Although the pandemic has accelerated digital transformation, little is known about the influence of digitalization on pandemic developments. Therefore, this country-level study aims to explore the impact of pre-pandemic digital adoption on COVID-19 outcomes and government measures. Using the Digital Adoption Index (DAI), we examined the association between countries' digital preparedness levels and COVID-19 cases, deaths, and stringency indices (SI) of government measures until March 2021. Gradient Tree Boosting based algorithm pinpointed essential features related to COVID-19 trends, such as digital adoption, populations' smoker fraction, age, and poverty. Subsequently, regression analyses indicated that higher DAI was associated with significant declines in new cases (β = - 362.25/pm; p < 0.001) and attributed deaths (β = - 5.53/pm; p < 0.001) months after the peak. When plotting DAI against the SI normalized for the starting day, countries with higher DAI adopted slightly more stringent government measures (β = 4.86; p < 0.01). Finally, a scoping review identified 70 publications providing valuable arguments for our findings. Countries with higher DAI before the pandemic show a positive trend in handling the pandemic and facilitate the implementation of more decisive governmental measures. Further distribution of digital adoption may have the potential to attenuate the impact of COVID-19 cases and deaths.

Modeling mobility, risk, and pandemic severity during the first year of COVID

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state's government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people's mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

The effectiveness of the restricted policy on specific venues in Hong Kong: A spatial point pattern view

After the fifth wave of the COVID-19 outbreak in May 2022, the Hong Kong government decided to ease the restrictions policy step by step. The main change was to re-open some venues that people like to visit and extend the hours of operation. With the implementation of the relaxed policy, however, the number of confirmed cases rose again. As a result, further relaxation was delayed. As an evaluation of the effectiveness of the restrictions policy could be a reference for future policies balancing viral spread and functionality of society, this paper aimed to respond to this question from the spatial point distribution view. The time, from late March 2020 to February 2021, during which the related policies took place was divided into six periods based on the policy trend (tightening or relaxing). The two-variable Ripley's Kfunction was applied for each period to explore the spatial dependence between confirmed cases and venues as changes in the spatial pattern can reveal the effect of the policy. The results show that, as time passed, the clustering degree decreased and reached its lowest level from August to mid-November 2020, then significantly increased, with the extent of clustering becoming more remarkable and the significant cluster size widening. Our results indicate that the policy had a positive effect on suppressing the spread of the virus in mid-July 2020. Then, with the virus infiltrating the community, the policy had little impact on containing the virus but likely contributed to avoid further infection.

Late Surges in COVID-19 Cases and Varying Transmission Potential Partially Due to Public Health Policy Changes in 5 Western States, March 10, 2020, to January 10, 2021

OBJECTIVE

This study investigates the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission potential in North Dakota, South Dakota, Montana, Wyoming, and Idaho from March 2020 through January 2021.

METHODS

Time-varying reproduction numbers, R _t , of a 7-d-sliding-window and of non-overlapping-windows between policy changes were estimated using the instantaneous reproduction number method. Linear regression was performed to evaluate if per-capita cumulative case-count varied across counties with different population size or density.

RESULTS

The median 7-d-sliding-window R _t estimates across the studied region varied between 1 and 1.25 during September through November 2020. Between November 13 and 18, R _t was reduced by 14.71% (95% credible interval, CrI, [14.41%, 14.99%]) in North Dakota following a mask mandate; Idaho saw a 1.93% (95% CrI [1.87%, 1.99%]) reduction and Montana saw a 9.63% (95% CrI [9.26%, 9.98%]) reduction following the tightening of restrictions. High-population and high-density counties had higher per-capita cumulative case-count in North Dakota on June 30, August 31, October 31, and December 31, 2020. In Idaho, North Dakota, South Dakota, and Wyoming, there were positive correlations between population size and per-capita weekly incident case-count, adjusted for calendar time and social vulnerability index variables.

CONCLUSIONS

R _t decreased after mask mandate during the region's case-count spike suggested reduction in SARS-CoV-2 transmission.

Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021

An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.

Impact of national lockdown on the suspected SARS-CoV-2 epidemic in terms of the number of fever cases in North Korea

After reopening the border, North Korea was experiencing community transmission of SARS-CoV-2 and implemented a nationwide lockdown. We estimated that the mean transmissibility declined to <1 within 5 days after the lockdown and that the lockdown was associated with a moderate decrease in transmissibility by 11% (95% confidence interval, 6–17%).

The impact of multi-level interventions on the second-wave SARS-CoV-2 transmission in China

Background

A re-emergence of COVID-19 occurred in the northeast of China in early 2021. Different levels of non-pharmaceutical interventions, from mass testing to city-level lockdown, were implemented to contain the transmission of SARS-CoV-2. Our study is aimed to evaluate the impact of multi-level control measures on the second-wave SARS-CoV-2 transmission in the most affected cities in China.

Methods

Five cities with over 100 reported COVID-19 cases within one month from Dec 2020 to Feb 2021 were included in our analysis. We fitted the exponential growth model to estimate basic reproduction number (R₀), and used a Bayesian approach to assess the dynamics of the time-varying reproduction number (R_t). We fitted linear regression lines on R_t estimates for comparing the decline rates of R_t across cities, and the slopes were tested by analysis of covariance. The effect of non-pharmaceutical interventions (NPIs) was quantified by relative R_t reduction and statistically compared by analysis of variance.

Results

A total of 2,609 COVID-19 cases were analyzed in this study. We estimated that R₀ all exceeded 1, with the highest value of 3.63 (1.36, 8.53) in Haerbin and the lowest value of 2.45 (1.44, 3.98) in Shijiazhuang. Downward trends of R_t were found in all cities, and the starting time of R_t < 1 was around the 12th day of the first local COVID-19 cases. Statistical tests on regression slopes of R_t and effect of NPIs both showed no significant difference across five cities (P = 0.126 and 0.157).

Conclusion

Timely implemented NPIs could control the transmission of SARS-CoV-2 with low-intensity measures for places where population immunity has not been established.

Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps

The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling. Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020. Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number (R0). We developed a travel network density parameter βt to provide an estimate of NPI impact on mobility patterns. Given the infection-fatality ratio and time lag (onset-to-death), a Bayesian probabilistic model is adapted to calculate the change in epidemic development with βt. Results showed that the change in βt clearly impacted R0. The three lockdowns strongly affected the growth of transmission rate and collectively reduced R0 by 78% before the restrictions were eased. The model forecasted daily infections and deaths by 2% and 3% fractional errors. It also projected what-if scenarios for different implementation protocols of each NPI. The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants, as well as for future pandemics.

Mathematical modeling for COVID-19 with focus on intervention strategies and cost-effectiveness analysis

The realistic assessments of public health intervention strategies are of great significance to effectively combat the COVID-19 epidemic and the formation of intervention policy. In this paper, an extended COVID-19 epidemic model is devised to assess the severity of the pandemic and explore effective control strategies. The model is characterized by ordinary differential equations with seven-state variables, and it incorporates some parameters associated with the interventions (i.e., media publicity, home isolation, vaccination and face-mask wearing) to investigate the impacts of these interventions on the spread of the COVID-19 epidemic. Some dynamic behaviors of the model, such as forward and backward bifurcation, are analyzed. Specifically, we calibrate the model parameters using actual COVID-19 infected data in Brazil by Markov Chain Monte Carlo algorithm such that we can study the effects of interventions on a practical case. Through a comprehensive exploration of model design and analysis, model calibration, sensitivity analysis, implementation of optimal control problems and cost-effectiveness analysis, the rationality of our model is verified, and the effective strategies to combat the epidemic in Brazil are revealed. The results show that the asymptomatic infected individuals are the main drivers of COVID-19 transmission, and rapid detection of asymptomatic infections is critical to combat the COVID-19 epidemic in Brazil. Interestingly, the effect of the vaccination rate associated with pharmaceutical intervention on the basic reproduction number is much lower than that of non-pharmaceutical interventions (NPIs). Our study also highlights the importance of media publicity. To reduce the infected individuals, the multi-pronged NPIs have considerable positive effects on controlling the outbreak of COVID-19. The infections are significantly decreased by the early implementation of media publicity complemented with home isolation and face-mask wearing strategy. When the cost of implementation is taken into account, the early implementation of media publicity complemented with a face-mask wearing strategy can significantly mitigate the second wave of the epidemic in Brazil. These results provide some management implications for controlling COVID-19.

Reconstructing the course of the COVID-19 epidemic over 2020 for US states and counties: Results of a Bayesian evidence synthesis model

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Decoupling between SARS-CoV-2 transmissibility and population mobility associated with increasing immunity from vaccination and infection in South America

All South American countries from the Southern cone (Argentina, Brazil, Chile, Paraguay and Uruguay) experienced severe COVID-19 epidemic waves during early 2021 driven by the expansion of variants Gamma and Lambda, however, there was an improvement in different epidemic indicators since June 2021. To investigate the impact of national vaccination programs and natural infection on viral transmission in those South American countries, we analyzed the coupling between population mobility and the viral effective reproduction number [Formula: see text]. Our analyses reveal that population mobility was highly correlated with viral [Formula: see text] from January to May 2021 in all countries analyzed; but a clear decoupling occurred since May-June 2021, when the rate of viral spread started to be lower than expected from the levels of social interactions. These findings support that populations from the South American Southern cone probably achieved the conditional herd immunity threshold to contain the spread of regional SARS-CoV-2 variants circulating at that time.

The heterogeneous regional effect of mobility on Coronavirus spread

The Coronavirus (COVID-19) pandemic struck global society in 2020. The pandemic required the adoption of public policies to control spread of the virus, underlining the mobility restrictions. Several studies show that these measures have been effective. Within the topic of Coronavirus spread, this original paper analyses the effect of mobility on Coronavirus spread in a heterogeneous regional context. A multiple dynamic regression model is used to control sub-national disparities in the effect of mobility on the spread of the Coronavirus, as well as to measure it at the context of Spanish regions. The model includes other relevant explanatory factors, such as wind speed, sunshine hours, vaccinated population and social awareness. It also develops a new methodology to optimise the use of Google trends data. The results reveal heterogeneity among regions, which has important implications for current and future pandemic containment strategies.

Contact tracing-induced Allee effect in disease dynamics

Contact tracing, case isolation, quarantine, social distancing, and other non-pharmaceutical interventions (NPIs) have been a cornerstone in managing the COVID-19 pandemic. However, their effects on disease dynamics are not fully understood. Saturation of contact tracing caused by the increase of infected individuals has been recognized as a crucial variable by healthcare systems worldwide. Here, we model this saturation process with a mechanistic and a phenomenological model and show that it induces an Allee effect which could determine an infection threshold between two alternative states—containment and outbreak. This transition was considered elsewhere as a response to the strength of NPIs, but here we show that they may be also determined by the number of infected individuals. As a consequence, timing of NPIs implementation and relaxation after containment is critical to their effectiveness. Containment strategies such as vaccination or mobility restriction may interact with contact tracing-induced Allee effect. Each strategy in isolation tends to show diminishing returns, with a less than proportional effect of the intervention on disease containment. However, when combined, their suppressing potential is enhanced. Relaxation of NPIs after disease containment--e.g. because vaccination--have to be performed in attention to avoid crossing the infection threshold required to a novel outbreak. The recognition of a contact tracing-induced Allee effect, its interaction with other NPIs and vaccination, and the existence of tipping points contributes to the understanding of several features of disease dynamics and its response to containment interventions. This knowledge may be of relevance for explaining the dynamics of diseases in different regions and, more importantly, as input for guiding the use of NPIs, vaccination campaigns, and its combination for the management of epidemic outbreaks.

Reconciling public health common good and individual privacy: new methods and issues in geoprivacy

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic.

Data science approaches to confronting the COVID-19 pandemic: a narrative review

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

nSpatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November, 2020

Purpose

Methods

Results

Conclusions

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.

Remote Learning of COVID-19 Kinetic Analysis in a Physical Chemistry Laboratory Class

The COVID-19 pandemic has affected many in-person laboratory courses across the world. The viral spreading model is complicated but parameters, such as its reproduction number, R _t, can be estimated with the susceptible, infectious, or recovered model. COVID-19 data for many states and countries are widely available online. This provides an opportunity for the students to analyze its spreading kinetics remotely. Here, we reported a laboratory set up online during the third week of the spring semester of 2021 to minimize social contacts. Due to the wide interest in developing online physical chemistry and analytical laboratories during the pandemic, we would like to share this laboratory design. The method, technique, procedure, and grading are described in this report. The student participants were able to apply the kinetic techniques learned in physical chemistry to successfully analyze an ongoing real-world problem through a remote learning environment and prepare this report.

Establishment of epidemic early warning index system and optimization of infectious disease model: Analysis on monitoring data of public health emergencies

The ability to mitigate the damages caused by emergencies is an important symbol of the modernization of an emergency capability. When responding to emergencies, government agencies and decision makers need more information sources to estimate the possible evolution of the disaster in a more efficient manner. In this paper, an optimization model for predicting the dynamic evolution of COVID-19 is presented by combining the propagation algorithm of system dynamics with the warning indicators. By adding new parameters and taking the country as the research object, the epidemic situation in countries such as China, Japan, Korea, the United States and the United Kingdom was simulated and predicted, the impact of prevention and control measures such as effective contact coefficient on the epidemic situation was analyzed, and the effective contact coefficient of the country was analyzed. The paper strives to provide early warning of emergencies scientifically and effectively through the combination of these two technologies, and put forward feasible references for the implementation of various countermeasures. Judging from the conclusion, this study reaffirmed the importance of responding quickly to public health emergencies and formulating prevention and control policies to reduce population exposure and prevent the spread of the pandemic.

The effect of population mobility on COVID-19 incidence in 314 Latin American cities: a longitudinal ecological study with mobile phone location data

BACKGROUND

Little is known about the effect of changes in mobility at the subcity level on subsequent COVID-19 incidence, which is particularly relevant in Latin America, where substantial barriers prevent COVID-19 vaccine access and non-pharmaceutical interventions are essential to mitigation efforts. We aimed to examine the longitudinal associations between population mobility and COVID-19 incidence at the subcity level across a large number of Latin American cities.

METHODS

In this longitudinal ecological study, we compiled aggregated mobile phone location data, daily confirmed COVID-19 cases, and features of urban and social environments to analyse population mobility and COVID-19 incidence at the subcity level among cities with more than 100 000 inhabitants in Argentina, Brazil, Colombia, Guatemala, and Mexico, from March 2 to Aug 29, 2020. Spatially aggregated mobile phone data were provided by the UN Development Programme in Latin America and the Caribbean and Grandata; confirmed COVID-19 cases were from national government reports and population and socioeconomic factors were from the latest national census in each country. We used mixed-effects negative binomial regression for a time-series analysis, to examine longitudinal associations between weekly mobility changes from baseline (prepandemic week of March 2-9, 2020) and subsequent COVID-19 incidence (lagged by 1-6 weeks) at the subcity level, adjusting for urban environmental and socioeconomic factors (time-invariant educational attainment, residential overcrowding, population density [all at the subcity level], and country).

FINDINGS

We included 1031 subcity areas, representing 314 Latin American cities, in Argentina (107 subcity areas), Brazil (416), Colombia (82), Guatemala (20), and Mexico (406). In the main adjusted model, we observed an incidence rate ratio (IRR) of 2·35 (95% CI 2·12-2·60) for COVID-19 incidence per log unit increase in the mobility ratio (vs baseline) during the previous week. Thus, 10% lower weekly mobility was associated with 8·6% (95% CI 7·6-9·6) lower incidence of COVID-19 in the following week. This association gradually weakened as the lag between mobility and COVID-19 incidence increased and was not different from null at a 6-week lag.

INTERPRETATION

Reduced population movement within a subcity area is associated with a subsequent decrease in COVID-19 incidence among residents of that subcity area. Policies that reduce population mobility at the subcity level might be an effective COVID-19 mitigation strategy, although they should be combined with strategies that mitigate any adverse social and economic consequences of reduced mobility for the most vulnerable groups.

FUNDING

Wellcome Trust.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55×10⁶ Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500×500m² grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.

Effects of adjusting public health, travel, and social measures during the roll-out of COVID-19 vaccination: a modelling study

BACKGROUND

Since the emergence of the COVID-19 pandemic in late 2019, various public health and social measures (PHSMs) have been used to suppress and mitigate the spread of SARS-CoV-2. With mass vaccination programmes against COVID-19 being rolled out in many countries in early 2021, we aimed to evaluate to what extent travel restrictions and other PHSMs can be relaxed without exacerbating the local and global spread of COVID-19.

METHODS

We adapted an existing age-structured susceptible-infectious-removed model of SARS-CoV-2 transmission dynamics that can be parameterised with country-specific age demographics and contact patterns to simulate the effect of vaccination and PHSM relaxation on transmission. We varied assumptions by age-specific susceptibility and infectiousness, vaccine uptake, contact patterns, and age structures. We used Hong Kong as a case study and assumed that, before vaccination, the population is completely susceptible to SARS-CoV-2 infection. We applied our model to 304 jurisdictions (27 countries and 277 sub-national administrative regions from eight countries). We assumed that PHSMs have suppressed the effective reproductive number (Re) to fall between 1·0 and 9·0 locally before the commencement of vaccination programmes. We evaluated the levels of PHSMs that should be maintained during the roll-out of COVID-19 vaccination to avoid a large local outbreak of COVID-19, with different assumptions about vaccine efficacy, vaccination coverage, and travel restrictions. We assumed that the maximum capacity of the health system, in terms of daily hospital admissions, is 0·005% of the population size.

FINDINGS

At vaccine efficacy of 0·80 in reducing susceptibility to SARS-CoV-2 infection, 0·50 in reducing SARS-CoV-2 infectivity, and 0·95 in reducing symptomatic COVID-19 diseases, vaccination coverage would have to be 100% for all individuals aged 30 or older to avoid an outbreak, when relaxing PHSMs, that would overload the local health-care system, assuming a pre-vaccination Re of 2·5. Testing and quarantine of at least 5 days would have to be maintained for inbound travellers to minimise the risk of reintroducing a local outbreak until high vaccination coverages are attained locally and overseas in most countries.

INTERPRETATION

Gradual relaxation of PHSMs should be carefully planned during the roll-out of vaccination programmes, and easing of travel restrictions weighed against risk of reintroducing outbreaks, to avoid overwhelming health systems and minimise deaths related to COVID-19.

FUNDING

Health and Medical Research Fund and the General Research Fund.