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

Evaluating the Effect of Public Health and Social Measures Under Rapid Changes in Population-level Immunity Against SARS-CoV-2: A Mathematical Modeling Study

BACKGROUND

Public health and social measures are crucial for controlling the spread of pathogens. However, well-tailored assessments of their impact remain elusive, particularly considering time-varying immunity established from prior exposures and its waning.

METHODS

We developed a mathematical model to estimate the time-varying basic reproduction number, accounting for the dynamics of underlying immunity. Applying this framework, we retrospectively assessed the impact of public health and social measures implemented from November 2021 to April 2022 on SARS-CoV-2 transmission in Korea and discussed potential biases from ignoring underlying immunity.

RESULTS

Our proposed model estimated a notable attenuation in the impact of public health on social measures on SARS-CoV-2 transmission in Korea with the emergence of the Omicron variants while remaining effective throughout the Delta and Omicron periods. These changes during the Omicron period became evident only upon adjusting for underlying immunity and were correlated with observed human mobility patterns in Korea.

CONCLUSIONS

Our findings support the importance of incorporating underlying immunity in evaluating public health and social measures, particularly in the presence of substantial changes over a short period, such as widespread infections or vaccination. This model would stand as a tool for informing public health planning, capable of mitigating the overall disease burden in future epidemics.

A novel approach to forecasting reproduction numbers of spatiotemporal stochastic epidemic spread using a PDE-based model and real-time infection data

The COVID-19 pandemic highlighted the need for improved epidemic spread forecasting, a critical precursor for developing optimal control measures for spread mitigation. Well-recognized shortcomings in computing basic and effective reproduction numbers ( R 0 , R e )-fundamental metrics for forecasting-underscore the need for new methods for estimating them from available data. We present a novel computational framework for estimating reproduction numbers from empirical spread data. The framework is derived from a mechanistic, spatiotemporal, Partial Differential Equation (PDE) model of epidemic spread utilizing mathematical results from PDE epidemic models. Forecasts of spatiotemporal effective reproduction number R e using the framework are found to be in excellent agreement with COVID-19 spread trends for Hamilton County, Ohio, USA, for three distinct periods. Furthermore, the forecasts are shown to align with corresponding reproduction numbers computed independently using the Wallinga-Teunis and Cori retrospective methods used in epidemiology. In summary, the results establish the validity of the framework and indicate applicability to future epidemics-especially for regions such as counties and for timeframes extending in weeks-even during dynamic phases when obtainable real-time infection spread data will likely be sparse.

EpiFusion: Joint inference of the effective reproduction number by integrating phylodynamic and epidemiological modelling with particle filtering

Accurately estimating the effective reproduction number (Rt) of a circulating pathogen is a fundamental challenge in the study of infectious disease. The fields of epidemiology and pathogen phylodynamics both share this goal, but to date, methodologies and data employed by each remain largely distinct. Here we present EpiFusion: a joint approach that can be used to harness the complementary strengths of each field to improve estimation of outbreak dynamics for large and poorly sampled epidemics, such as arboviral or respiratory virus outbreaks, and validate it for retrospective analysis. We propose a model of Rt that estimates outbreak trajectories conditional upon both phylodynamic (time-scaled trees estimated from genetic sequences) and epidemiological (case incidence) data. We simulate stochastic outbreak trajectories that are weighted according to epidemiological and phylodynamic observation models and fit using particle Markov Chain Monte Carlo. To assess performance, we test EpiFusion on simulated outbreaks in which transmission and/or surveillance rapidly changes and find that using EpiFusion to combine epidemiological and phylodynamic data maintains accuracy and increases certainty in trajectory and Rt estimates, compared to when each data type is used alone. We benchmark EpiFusion's performance against existing methods to estimate Rt and demonstrate advances in speed and accuracy. Importantly, our approach scales efficiently with dataset size. Finally, we apply our model to estimate Rt during the 2014 Ebola outbreak in Sierra Leone. EpiFusion is designed to accommodate future extensions that will improve its utility, such as explicitly modelling population structure, accommodations for phylogenetic uncertainty, and the ability to weight the contributions of genomic or case incidence to the inference.

Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study

OBJECTIVES

Although the role of specific holidays in modifying transmission dynamics of infectious diseases has received some research attention, the epidemiological impact of public holidays on the transmission of coronavirus disease 2019 (COVID-19) remains unclear.

METHODS

To assess the extent of increased transmission frequency during public holidays, we collected COVID-19 incidence and mobility data in Hokkaido, Tokyo, Aichi, and Osaka from February 15, 2020 to September 30, 2021. Models linking the estimated effective reproduction number (Rt) with raw or adjusted mobility, public holidays, and the state of emergency declaration were developed. The best-fit model included public holidays as an essential input variable, and was used to calculate counterfactuals of Rt in the absence of holidays.

RESULTS

During public holidays, on average, Rt increased by 5.71%, 3.19%, 4.84%, and 24.82% in Hokkaido, Tokyo, Aichi, and Osaka, respectively, resulting in a total increase of 580 (95% confidence interval [CI], 213 to 954), 2,209 (95% CI, 1,230 to 3,201), 1,086 (95% CI, 478 to 1,686), and 5,211 (95% CI, 4,554 to 5,867) cases that were attributable to the impact of public holidays.

CONCLUSIONS

Public holidays intensified the transmission of COVID-19, highlighting the importance of considering public holidays in designing appropriate public health and social measures in the future.

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.

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.

Evaluating the COVID-19 vaccination program in Japan, 2021 using the counterfactual reproduction number

Japan implemented its nationwide vaccination program against COVID-19 in 2021, immunizing more than one million people (approximately 1%) a day. However, the direct and indirect impacts of the program at the population level have yet to be fully evaluated. To assess the vaccine effectiveness during the Delta variant (B.1.617.2) epidemic in 2021, we used a renewal process model. A transmission model was fitted to the confirmed cases from 17 February to 30 November 2021. In the absence of vaccination, the cumulative numbers of infections and deaths during the study period were estimated to be 63.3 million (95% confidence interval [CI] 63.2-63.6) and 364,000 (95% CI 363-366), respectively; the actual numbers of infections and deaths were 4.7 million and 10,000, respectively. Were the vaccination implemented 14 days earlier, there could have been 54% and 48% fewer cases and deaths, respectively, than the actual numbers. We demonstrated the very high effectiveness of COVID-19 vaccination in Japan during 2021, which reduced mortality by more than 97% compared with the counterfactual scenario. The timing of expanding vaccination and vaccine recipients could be key to mitigating the disease burden of COVID-19. Rapid and proper decision making based on firm epidemiological input is vital.

Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan

Background

Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture.

Methods

In Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion.

Results

In the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time.

Conclusion

Our results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.

Modeling the time-dependent transmission rate using gaussian pulses for analyzing the COVID-19 outbreaks in the world

In this work, an SEIR epidemic model with time-dependent transmission rate parameters for the multiple waves of COVID-19 infection was investigated. It is assumed that the transmission rate is determined by the superposition of the Gaussian pulses. The interaction of these dynamics is represented by recursive equations. Analysis of the overall dynamics of disease spread is determined by the effective reproduction number R_e(t) produced throughout the infection period. The study managed to show the evolution of the epidemic over time and provided important information about the occurrence of multiple waves of COVID-19 infection in the world and Indonesia.

Go To Travel campaign and the geographic spread of COVID-19 in Japan

Background

In 2020, the Japanese government implemented first of two Go To Travel campaigns to promote the tourism sector as well as eating and drinking establishments, especially in remote areas. The present study aimed to explore the relationship between enhanced travel and geographic propagation of COVID-19 across Japan, focusing on the second campaign with nationwide large-scale economic boost in 2020.

Methods

We carried out an interrupted time-series analysis to identify the possible cause-outcome relationship between the Go To Travel campaign and the spread of infection to nonurban areas in Japan. Specifically, we counted the number of prefectures that experienced a weekly incidence of three, five, and seven COVID-19 cases or more per 100,000 population, and we compared the rate of change before and after the campaign.

Results

Three threshold values and three different models identified an increasing number of prefectures above the threshold, indicating that the inter-prefectural spread intensified following the launch of the second Go To Travel campaign from October 1st, 2020. The simplest model that accounted for an increase in the rate of change only provided the best fit. We estimated that 0.24 (95% confidence interval 0.15 to 0.34) additional prefectures newly exceeded five COVID-19 cases per 100,000 population per week during the second campaign.

Conclusions

The enhanced movement resulting from the Go To Travel campaign facilitated spatial spread of COVID-19 from urban to nonurban locations, where health-care capacity may have been limited.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07799-0.

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.

Response to COVID-19 during the Tokyo Olympic Games: Did we properly assess the risk?

Background

The number of coronavirus disease 2019 (COVID-19) cases was expected to increase during the Tokyo Olympic Games because of the increased physical contact within and between the domestic population and international participants of the Games. The rapid rise of the Delta variant (B.1.617) in Japan meant that hosting the Olympic Games without any restrictions was likely to lead to an increase in cases. We aimed to quantitatively assess possible COVID-19 response strategies for the Olympic Games, comparing the prevalence of severe cases and the cumulative number of COVID-19 deaths via scenario analysis.

Methods

We used a discrete-time deterministic compartmental model structured by age group. Parameters were calibrated using the age-stratified COVID-19 incidence data in Osaka. Numerical simulations incorporated the planned Olympics Games and nationwide COVID-19 vaccination into the proposed model, alongside various subjects and types of countermeasures.

Results

Our model-informed approach suggested that having spectators at the Tokyo Olympic Games could lead to a surge in both cases and hospitalization. Projections for the scenario that explicitly incorporated the spread of the Delta variant (i.e., time-dependent increase in the relative transmissibility) showed that imposing stringent social distancing measures (Rt=0.7) for more than 8 weeks from the end of the Olympic Games might be required to suppress the prevalence of severe cases of COVID-19 to avoid overwhelming the intensive care unit capacity in Tokyo.

Conclusions

Our modeling analyses guided an optimal choice of COVID-19 response during and after the Tokyo Olympic Games, allowing the epidemic to be brought under control despite such a large mass gathering.

Assessing Public Health and Social Measures Against COVID-19 in Japan From March to June 2021

Background

Public health and social measures (PHSM) against COVID-19 in Japan involve requesting the public to voluntarily reduce social contact; these measures are not legally binding. The effectiveness of such PHSM has been questioned with emergence of the SARS-CoV-2 Alpha variant (B.1.1.7), which exhibited elevated transmissibility.

Materials and Methods

We investigated the epidemic dynamics during the fourth epidemic wave in Japan from March to June 2021 involving pre-emergency measures and declaration of a state of emergency (SoE). We estimated the effective reproduction number (R_t) before and after these interventions, and then analyzed the relationship between lower R_t values and each PHSM.

Results

With implementation of pre-emergency measures (PEM) in 16 prefectures, the R_t was estimated to be < 1 in six prefectures; its average relative reduction ranged from 2 to 19%. During the SoE, 8 of 10 prefectures had an estimated R_t < 1, and the average relative reduction was 26%–39%. No single intervention was identified that uniquely resulted in an R_t value < 1.

Conclusion

An SoE can substantially reduce the R_t and may be required to curb a surge in cases caused by future SARS-CoV-2 variants of concern with elevated transmissibility. More customized interventions did not reduce the R_t value to < 1 in this study, but that may be partly attributable to the greater transmissibility of the Alpha variant.

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.

Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models

Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China, in December 2019, became a global pandemic within months due to its high transmissibility. In the absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPIs) to contain the spread of COVID-19 brought the entire world to a halt. After almost a year of seemingly returning to normalcy with the world's quickest vaccine development, the emergence of more infectious and vaccine resistant coronavirus variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to portray the possible scenarios based on the three key factors: impact of interventions (pharmaceutical and NPIs), vaccination rate, and vaccine efficacy. In our study, we assessed two of the most crucial factors, transmissibility and vaccination rate, in order to reduce the spreading of COVID-19 in a simple but effective manner. In order to incorporate the time-varying mutational landscape of COVID-19 variants, we estimated a weighted transmissibility composed of the proportion of existing strains that naturally vary over time. Additionally, we consider time varying vaccination rates based on the number of daily new cases. Our method for calculating the vaccination rate from past active cases is an effective approach in forecasting probable future scenarios as it actively tracks people's attitudes toward immunization as active case changes. Our simulations show that if a large number of individuals cannot be vaccinated by ensuring high efficacy in a short period of time, adopting NPIs is the best approach to manage disease transmission with the emergence of new vaccine breakthrough and more infectious variants.

Associations between components of household expenditures and the rate of change in the number of new confirmed cases of COVID-19 in Japan: Time-series analysis

Background

Social distancing measures to prevent the spread of COVID-19 included restrictions on retail services in many countries. In some countries, the governments also subsidized consumer spending on part of retail services to help struggling businesses. To evaluate the costs and benefits of government interventions in retail services, it is necessary to measure the infectiousness of each type of consumer activity.

Methods

This study regresses the log difference over seven days in the number of new confirmed cases of COVID-19 in Japan on lagged values of household expenditures per household on eating out, traveling, admissions to entertainment facilities, clothing and footwear, and the other items, as well as a measure of mobility in public transportation in the past 14 days. The sample period of the dependent variable is set from March 1, 2020, to February 1, 2021, in order to avoid a possible structural break due to the spread of mutant strains in 2021. The regression model is estimated by the Bayesian method with a non-informative (improper) prior. The estimated model is evaluated by out-of-sample forecast performance from February 2, 2021, onward.

Results

The out-of-sample forecasts of the regression by the posterior means of regression coefficients perform well before the spread of the Delta variant in Japan since June 2021. R² for the out-of-sample forecasts from February 2, 2021, to June 30, 2021, is 0.60. The dependent variable of the regression overshot the out-of-sample forecasts from mid-June to August 2021. Then, the out-of-sample forecasts overpredicted the dependent variable for the rest of 2021.

Conclusion

The estimated model can be potentially useful in simulating changes in the number of new confirmed cases due to household spending on retail services, if it can be adjusted to real-time developments of mutant strains and vaccinations. Such simulations would help in designing cost-efficient government interventions.

Epidemiology of coronavirus disease 2019 (COVID-19) in Japan during the first and second waves

Following the emergence and worldwide spread of coronavirus disease 2019 (COVID-19), each country has attempted to control the disease in different ways. The first patient with COVID-19 in Japan was diagnosed on 15 January 2020, and until 31 October 2020, the epidemic was characterized by two large waves. To prevent the first wave, the Japanese government imposed several control measures such as advising the public to avoid the 3Cs (closed spaces with poor ventilation, crowded places with many people nearby, and close-contact settings such as close-range conversations) and implementation of "cluster buster" strategies. After a major epidemic occurred in April 2020 (the first wave), Japan asked its citizens to limit their numbers of physical contacts and announced a non-legally binding state of emergency. Following a drop in the number of diagnosed cases, the state of emergency was gradually relaxed and then lifted in all prefectures of Japan by 25 May 2020. However, the development of another major epidemic (the second wave) could not be prevented because of continued chains of transmission, especially in urban locations. The present study aimed to descriptively examine propagation of the COVID-19 epidemic in Japan with respect to time, age, space, and interventions implemented during the first and second waves. Using publicly available data, we calculated the effective reproduction number and its associations with the timing of measures imposed to suppress transmission. Finally, we crudely calculated the proportions of severe and fatal COVID-19 cases during the first and second waves. Our analysis identified key characteristics of COVID-19, including density dependence and also the age dependence in the risk of severe outcomes. We also identified that the effective reproduction number during the state of emergency was maintained below the value of 1 during the first wave.

Modeling COVID-19 Incidence by the Renewal Equation after Removal of Administrative Bias and Noise

Simple Summary

In the past two years, the COVID-19 incidence curves and reproduction number Rt have been the main metrics used by policy makers and journalists to monitor the spread of this global pandemic. However, these metrics are not always reliable in the short term, because of a combination of delay in detection, administrative delays and random noise. In this article, we present a complete model of COVID-19 incidence, faithfully reconstructing the incidence curve and reproduction number from the renewal equation of the disease and precisely estimating the biases associated with periodic weekly bias, festive day bias and residual noise.

Abstract

The sanitary crisis of the past two years has focused the public’s attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number Rt, defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable Rt. Using Rt, a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve.

Decrease in overdispersed secondary transmission of COVID-19 over time in Japan

Coronavirus disease 2019 (COVID-19) has been described as having an overdispersed offspring distribution, i.e. high variation in the number of secondary transmissions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) per single primary COVID-19 case. Accordingly, countermeasures focused on high-risk settings and contact tracing could efficiently reduce secondary transmissions. However, as variants of concern with elevated transmissibility continue to emerge, controlling COVID-19 with such focused approaches has become difficult. It is vital to quantify temporal variations in the offspring distribution dispersibility. Here, we investigated offspring distributions for periods when the ancestral variant was still dominant (summer, 2020; wave 2) and when Alpha variant (B.1.1.7) was prevailing (spring, 2021; wave 4). The dispersion parameter (k) was estimated by analysing contact tracing data and fitting a negative binomial distribution to empirically observed offspring distributions from Nagano, Japan. The offspring distribution was less dispersed in wave 4 (k = 0.32; 95% confidence interval (CI) 0.24-0.43) than in wave 2 (k = 0.21 (95% CI 0.13-0.36)). A high proportion of household transmission was observed in wave 4, although the proportion of secondary transmissions generating more than five secondary cases did not vary over time. With this decreased variation, the effectiveness of risk group-focused interventions may be diminished.