Impacts of income inequality and the mediation role of reporting delays on COVID-19 deaths during 2020 and 2021 in Hong Kong: an observational study.

OBJECTIVE: To estimate the impacts of demographic factors and income disparities on the case fatality rate (CFR) of COVID-19 in Hong Kong, taking into account the influence of reporting delays (ie, the duration between symptom onset and case confirmation). DESIGN: Retrospective observational longitudinal study. PARTICIPANTS: A total of 7406 symptomatic patients with residence information reported between 23 January 2020 and 2 October 2021. MAIN OUTCOME MEASURES: The study examined the disparity in COVID-19 deaths associated with the factors such as age (≥65 vs 0-64 years old groups), gender and the income level of districts (low income vs non-low income). The severe reporting delay (>10 days) was considered as the mediator for mediation analysis. A Cox proportional hazards regression model was constructed. RESULTS: We found that CFR was 3.07% in the low-income region, twofold higher than 1.34% in the other regions. Although the severe reporting delay was associated with a hazard ratio (HR) of about 1.9, its mediation effect was only weakly present for age, but not for gender or income level. Hence, high CFR in Hong Kong was largely attributed to the direct effects of the elderly (HR 25.967; 95% CI 14.254 to 47.306) and low income (HR 1.558; 95% CI 1.122 to 2.164). CONCLUSION: The disparity in COVID-19 deaths between income regions is not due to reporting delays, but rather to health inequities in Hong Kong. These risks may persist after the discontinuation of test-and-trace measures and extend to other high-threat respiratory pathogens. Urgent actions are required to identify vulnerable groups in low-income regions and understand the underlying causes of health inequities.

Association of air pollution and weather conditions during infection course with COVID-19 case fatality rate in the United Kingdom.

Although the relationship between the environmental factors, such as weather conditions and air pollution, and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed. We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]) using data between March 26, 2020 and September 15, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria and the closeness between the predicted and observed values of CFR. The risk of death reached its maximum level when the low temperature (6 °C) occurred 1 day before (OR 1.59; 95% CI 1.52-1.63), prolonged sunlight duration (11-14 h) 3 days after (OR 1.24; 95% CI 1.18-1.30) and increased [Formula: see text] (19 µg/m3) 1 day after the onset of symptom (OR 1.12; 95% CI 1.09-1.16). After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

Predictive model of chemotherapy-related toxicity in elderly Chinese cancer patients.

Purpose: Older cancer patients are more likely to develop and die from chemotherapy-related toxicity. However, evidence on drug safety and optimal effective doses is relatively limited in this group. The aim of this study was to develop a tool to identify elderly patients vulnerable to chemotherapy toxicity. Patients and methods: Elderly cancer patients ≥60 years old who visited the oncology department of Peking Union Medical College Hospital between 2008 and 2012 were included. Each round of chemotherapy was regarded as a separate case. Clinical factors included age, gender, physical status, chemotherapy regimen and laboratory tests results were recorded. Severe (grade ≥3) chemotherapy-related toxicity of each case was captured according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0. Univariate analysis was performed by chi-square statistics to determine which factors were significantly associated with severe chemotherapy toxicity. Logistic regression was used to build the predictive model. The prediction model was validated by calculating the area under the curve of receiver operating characteristic (ROC). Results: A total of 253 patients and 1,770 cases were included. The average age of the patients was 68.9 years. The incidence of grade 3-5 adverse events was 24.17%. Cancer type (non-GI cancers), BMI<20 kg/m2, KPS<90%, severe comorbidity, polychemotherapy, standard dose chemotherapy, low white blood cells count, anemia, low platelet cells count, low creatine level and hypoalbuminemia were associated with severe chemotherapy-related toxicity. We used these factors to construct a chemotherapy toxicity prediction model and the area under the ROC curve was 0.723 (95% CI, 0.687-0.759). Risk of toxicity increased with higher risk score (11.98% low, 31.51% medium, 70.83% high risk; p < 0.001). Conclusion: We constructed a predictive model of chemotherapy toxicity in elderly cancer patients based on a Chinese population. The model can be used to guide clinicians to identify vulnerable population and adjust treatment regimens accordingly.

Impact of early phase COVID-19 precautionary behaviors on seasonal influenza in Hong Kong: A time-series modeling approach.

Background: Before major non-pharmaceutical interventions were implemented, seasonal incidence of influenza in Hong Kong showed a rapid and unexpected reduction immediately following the early spread of COVID-19 in mainland China in January 2020. This decline was presumably associated with precautionary behavioral changes (e.g., wearing face masks and avoiding crowded places). Knowing their effectiveness on the transmissibility of seasonal influenza can inform future influenza prevention strategies. Methods: We estimated the effective reproduction number (R t ) of seasonal influenza in 2019/20 winter using a time-series susceptible-infectious-recovered (TS-SIR) model with a Bayesian inference by integrated nested Laplace approximation (INLA). After taking account of changes in underreporting and herd immunity, the individual effects of the behavioral changes were quantified. Findings: The model-estimated mean R t reduced from 1.29 (95%CI, 1.27-1.32) to 0.73 (95%CI, 0.73-0.74) after the COVID-19 community spread began. Wearing face masks protected 17.4% of people (95%CI, 16.3-18.3%) from infections, having about half of the effect as avoiding crowded places (44.1%, 95%CI, 43.5-44.7%). Within the current model, if more than 85% of people had adopted both behaviors, the initial R t could have been less than 1. Conclusion: Our model results indicate that wearing face masks and avoiding crowded places could have potentially significant suppressive impacts on influenza.

A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.

Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.

Impacts of social distancing, rapid antigen test and vaccination on the Omicron outbreak during large temperature variations in Hong Kong: A modelling study.

BACKGROUND: The impacts of non-pharmaceutical interventions (NPIs) and vaccine boosters on the transmission of the largest outbreak of COVID-19 (the fifth wave) in Hong Kong have not been reported. The outbreak, dominated by the Omicron BA.2 subvariant, began to spread substantially after the Spring Festival in February, 2022, when the temperature varied greatly (e.g. a cold surge event). Tightening social distancing measures did not succeed in containing the outbreak until later with the use of rapid antigen tests (RAT) and increased vaccination rates. Temperature has been previously found to have significant impact on the transmissibility. Understanding how the public health interventions influence the number of infections in this outbreak provide important insights on prevention and control of COVID-19 during different seasons. METHODS: We developed a transmission model incorporating stratified immunity with vaccine-induced antibody responses and the daily changes in population mobility, vaccination and weather factors (i.e. temperature and relative humidity). We fitted the model to the daily reported cases detected by either PCR or RAT between 1 February and 31 March using Bayesian statistics, and quantified the effects of individual NPIs, vaccination and weather factors on transmission dynamics. RESULTS: Model predicted that, with the vaccine uptake, social distancing reduced the cumulative incidence (CI) from 58.2% to 44.5% on average. The use of RAT further reduced the CI to 39.0%. Without vaccine boosters in these two months, the CI increased to 49.1%. While public health interventions are important in reducing the total infections, the outbreak was temporarily driven by the cold surge. If the coldest two days (8.5 °C and 8.8 °C) in February were replaced by the average temperature in that month (15.2 °C), the CI would reduce from 39.0% to 28.2%. CONCLUSION: Preventing and preparing for the transmission of COVID-19 considering the change in temperature appears to be a cost-effective preventive strategy to lead people to return to normal life.

Path to normality: Assessing the level of social-distancing measures relaxation against antibody-resistant SARS-CoV-2 variants in a partially-vaccinated population.

Introduction: Two years into the coronavirus 2019 (COVID-19) pandemic, populations with less built-up immunity continued to devise ways to optimize social distancing measures (SDMs) relaxation levels for outbreaks triggered by SARS-CoV-2 and its variants to resume minimal economics activities while avoiding hospital system collapse. Method: An age-stratified compartmental model featuring social mixing patterns was first fitted the incidence data in second wave in Hong Kong. Hypothetical scenario analysis was conducted by varying population mobility and vaccination coverages (VCs) to predict the number of hospital and intensive-care unit admissions in outbreaks initiated by ancestral strain and its variants (Alpha, Beta, Gamma, Delta and Omicron). Scenarios were "unsustainable" if either of admissions was larger than the maximum of its occupancy. Results: At VC of 65%, scenarios of full SDMs relaxation (mean daily social encounters prior to COVID-19 pandemic = 14.1 contacts) for outbreaks triggered by ancestral strain, Alpha and Beta were sustainable. Restricting levels of SDMs was required such that the optimal population mobility had to be reduced to 0.9, 0.65 and 0.37 for Gamma, Delta and Omicron associated outbreaks respectively. VC improvement from 65% to 75% and 95% allowed complete SDMs relaxation in Gamma-, and Delta-driven epidemic respectively. However, this was not supported for Omicron-triggered epidemic. Discussion: To seek a path to normality, speedy vaccine and booster distribution to the majority across all age groups is the first step. Gradual or complete SDMs lift could be considered if the hybrid immunity could be achieved due to high vaccination coverage and natural infection rate among vaccinated or the COVID-19 case fatality rate could be reduced similar to that for seasonal influenza to secure hospital system sustainability.

Transmission Dynamics of Tuberculosis with Age-specific Disease Progression.

Demographic structure and latent phenomenon are two essential factors determining the rate of tuberculosis transmission. However, only a few mathematical models considered age structure coupling with disease stages of infectious individuals. This paper develops a system of delay partial differential equations to model tuberculosis transmission in a heterogeneous population. The system considers demographic structure coupling with the continuous development of disease stage, which is crucial for studying how aging affects tuberculosis dynamics and disease progression. Here, we determine the basic reproduction number, and several numerical simulations are used to investigate the influence of various progression rates on tuberculosis dynamics. Our results support that the aging effect on the disease progression rate contributes to tuberculosis permanence.

Assessment of the fatality rate and transmissibility taking account of undetected cases during an unprecedented COVID-19 surge in Taiwan.

BACKGROUND: During the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that many patients were not treated promptly or effectively. However, many unexplained deaths were subsequently identified as cases, indicating a few undetected cases, resulting in a higher estimate of FR. Whether the true FR is exceedingly high and what factors determine the detection of cases remain unknown. Estimating the true number of total infected cases (i.e. including undetected cases) can allow an accurate estimation of FR and effective reproduction number ([Formula: see text]). METHODS: We aimed at quantifying the time-varying FR and [Formula: see text] using the estimated true numbers of cases; and, exploring the relationship between the true case number and test and trace data. After adjusting for reporting delays, we developed a model to estimate the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and [Formula: see text] were calculated using the true number of cases. Afterwards, a logistic regression model was used to assess the impact of daily testing and tracing data on the detection ratio of deaths. RESULTS: The estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterwards. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. [Formula: see text] reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was found to be associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact traced before symptom onset. CONCLUSIONS: Increasing testing capacity and contact tracing coverage without delays not only improve parameter estimation by reducing hidden cases but may also reduce fatality rates.

Assessing the impact of temperature and humidity exposures during early infection stages on case-fatality of COVID-19: A modelling study in Europe.

Background Although associations between key weather indicators (i.e. temperature and humidity) and COVID-19 mortality have been reported, the relationship between these exposures at different timings in early infection stages (from virus exposure up to a few days after symptom onset) and the probability of death after infection (also called case fatality rate, CFR) has yet to be determined. Methods We estimated the instantaneous CFR of eight European countries using Bayesian inference in conjunction with stochastic transmission models, taking account of delays in reporting the number of newly confirmed cases and deaths. The exposure-lag-response associations between fatality rate and weather conditions to which patients were exposed at different timings were obtained using distributed lag nonlinear models coupled with mixed-effect models. Results Our results show that the Odds Ratio (OR) of death is negatively associated with the temperature, with two maxima (OR = 1.29 (95% CI: 1.23, 1.35) at -0.1°C; OR = 1.12 (95% CI: 1.08, 1.16) at 0.1°C) occurring at the time of virus exposure and after symptom onset. Two minima (OR = 0.81 (95% CI: 0.71, 0.92) at 23.2°C; OR = 0.71 (95% CI: 0.63, 0.80) at 21.7°C) also occurred at these two distinct periods correspondingly. Low humidity (below 50%) during the early stages and high humidity (approximately 89%) after symptom onset were related to the lower fatality. Conclusion Environmental conditions may affect not only the initial viral load when patients are exposed to the virus, but also individuals' immune response around symptom onset. Warmer temperatures and higher humidity after symptom onset were linked to lower fatality.

Equitable access to COVID-19 vaccines makes a life-saving difference to all countries.

Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HICs. To explore the consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we examine vaccine allocation strategies using a multistrain metapopulation model. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Sharper disparities in vaccine allocation between HICs and LMICs lead to earlier and larger outbreaks of new waves. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.

The impact of contact tracing and testing on controlling COVID-19 outbreak without lockdown in Hong Kong: An observational study.

BACKGROUND: Maintaining effective contact tracing to control COVID-19 is challenging. Rapid growth in the number of infected cases can overload tracing and testing capacity, resulting in failure to trace contacts and delays in confirming an infection until after symptom onset (confirmation delay), hence increasing transmissibility. A substantial outbreak in Hong Kong, which was suppressed with non-pharmaceutical interventions (NPIs), provided an opportunity to assess the impact of overloading contact tracing and of efforts to improve its efficiency. METHODS: Using epidemiological-link (epi-link) data, we calculated the probability and duration of confirmation delay for cases with and without an epi-link, among all 3,148 confirmed cases between 5 July and 15 August 2020. Logistic regression was performed to determine the relationship between the number of recently confirmed infections and the probability of confirmation delay for epi-linked (contact-traced) cases. We estimated the impact on this relationship of targeted testing of at-risk groups. FINDINGS: The probability and duration of confirmation delay were associated with the rise in daily case number during growth of the outbreak. The proportion with confirmation delay among contact-traced cases increased from about 60 % to nearly 85 % as the number of cases grew from 1 to 50 per day (p-value = 0.003). The subsequent introduction of testing services for at-risk groups substantially reduced the proportion and it did not approach 85 % again until the daily number of cases exceeded 125. This 2.5-fold improvement in capacity contributed crucially to suppression of the outbreak. INTERPRETATION: The number of recently confirmed infections is an indicator of the load on the contact-tracing system, the consequence of which can be assessed by the probability of confirmation delay. Measures to monitor and improve contact-tracing efficiency, alongside social distancing interventions, can enable outbreaks to be controlled without lockdown. FUNDING: City University of Hong Kong and Health and Medical Research Fund.

The impact of multiple non-pharmaceutical interventions on controlling COVID-19 outbreak without lockdown in Hong Kong: A modelling study.

BACKGROUND: The 'third wave' of COVID-19 in Hong Kong, China was suppressed by non-pharmaceutical interventions (NPIs). Although social distancing regulations were quickly strengthened, the outbreak continued to grow, causing increasing delays in tracing and testing. Further regulations were introduced, plus 'targeted testing' services for at-risk groups. Estimating the impact of individual NPIs could provide lessons about how outbreaks can be controlled without radical lockdown. However, the changing delays in confirmation time challenge current modelling methods. We used a novel approach aimed at disentangling and quantifying the effects of individual interventions. METHODS: We incorporated the causes of delays in tracing and testing (i.e. load-efficiency relationship) and the consequences from such delays (i.e. the proportion of un-traced cases and the proportion of traced-cases with confirmation delay) into a deterministic transmission model, which was fitted to the daily number of cases with and without an epi­link (an indication of being contact-traced). The effect of each NPI was then calculated. FINDINGS: The model estimated that after earlier relaxation of regulations, Re rose from 0.7 to 3.2. Restoration of social distancing to the previous state only reduced Re to 1.3, because of increased delay in confirmation caused by load on the contact-tracing system. However, Re decreased by 20.3% after the introduction of targeted testing and by 17.5% after extension of face-mask rules, reducing Re to 0.9 and suppressing the outbreak. The output of the model without incorporation of delay failed to capture important features of transmission and Re. INTERPRETATION: Changing delay in confirmation has a significant impact on disease transmission and estimation of transmissibility. This leads to a clear recommendation that delay should be monitored and mitigated during outbreaks, and that delay dynamics should be incorporated into models to assess the effects of NPIs. FUNDING: City University of Hong Kong and Health and Medical Research Fund.

Prediction of dengue annual incidence using seasonal climate variability in Bangladesh between 2000 and 2018.

The incidence of dengue has increased rapidly in Bangladesh since 2010 with an outbreak in 2018 reaching a historically high number of cases, 10,148. A better understanding of the effects of climate variability before dengue season on the increasing incidence of dengue in Bangladesh can enable early warning of future outbreaks. We developed a generalized linear model to predict the number of annual dengue cases based on monthly minimum temperature, rainfall and sunshine prior to dengue season. Variable selection and leave-one-out cross-validation were performed to identify the best prediction model and to evaluate the model's performance. Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912-10,276]), in addition to smaller outbreaks in five different years (2003, 2006, 2010, 2012 and 2014) and successfully identified the increasing trend in cases between 2010 and 2018. We found that temperature was positively associated with the annual incidence during the late winter months (between January and March) but negatively associated during the early summer (between April and June). Our results might be suggest an optimal minimum temperature for mosquito growth of 21-23°C. This study has implications for understanding how climate variability has affected recent dengue expansion in neighbours of Bangladesh (such as northern India and Southeast Asia).

Health-Promoting Behaviors, Risk Perceptions, and Attention to COVID-19-Related Information: Comparing People's Responses to the COVID-19 Pandemic Across Times of Chinese New Year and Summer 2020 in Hong Kong.

Background: Since the start of the COVID-19 pandemic, individuals have been encouraged to engage in health-promoting behaviors, namely actions taken to prevent infection and keep themselves healthy, such as maintaining social distancing. However, other factors, such as risk perception and feelings of fear, also might influence whether an individual takes such measures. This study compared people's responses to the pandemic in terms of their adoption of COVID-19 health-promoting behaviors, COVID-19 risk perceptions, and attention to COVID-19-related information during two periods: the 2020 Chinese New Year (CNY) in Hong Kong (HK), i.e., the very beginning of the COVID-19 outbreak (Time 1, T1), and summer 2020, i.e., before and during the third wave of COVID-19 infections in HK (Time 2, T2). Methods: Data were extracted from 180 HK participants, who were asked to recall and report their health-promoting behaviors, emotional and cognitive COVID-19 risk perceptions, and attention to COVID-19-related information during T1 and T2. A repeated-measures ANOVA series was conducted to investigate differences in public responses between the two aforementioned time points. Main Findings: After controlling for the effects from gender, age, and education levels, the participants reported practicing more infection-prevention behaviors, experiencing a lower level of fear as a psychological response, and paying less attention to COVID-19-related information during T2 than T1. Conclusions: This study addressed the need to monitor public responses to the COVID-19 pandemic, including changes in people's behaviors and psychological responses across time. The results also suggest that the HK public was steered toward striking a balance between strengthening their infection-prevention behaviors and reducing their fear of COVID-19 infection.

Evaluating the effectiveness of control measures in multiple regions during the early phase of the COVID-19 pandemic in 2020.

The number of COVID-19 confirmed cases rapidly grew since the SARS-CoV-2 virus was identified in late 2019. Due to the high transmissibility of this virus, more countries are experiencing the repeated waves of the COVID-19 pandemic. However, with limited manufacturing and distribution of vaccines, control measures might still be the most critical measures to contain outbreaks worldwide. Therefore, evaluating the effectiveness of various control measures is necessary to inform policymakers and improve future preparedness. In addition, there is an ongoing need to enhance our understanding of the epidemiological parameters and the transmission patterns for a better response to the COVID-19 pandemic. This review focuses on how various models were applied to guide the COVID-19 response by estimating key epidemiologic parameters and evaluating the effectiveness of control measures. We also discuss the insights obtained from the prediction of COVID-19 trajectories under different control measures scenarios.

Reopening International Borders without Quarantine: Contact Tracing Integrated Policy against COVID-19.

With the COVID-19 vaccination widely implemented in most countries, propelled by the need to revive the tourism economy, there is a growing prospect for relieving the social distancing regulation and reopening borders in tourism-oriented countries and regions. This need incentivizes stakeholders to develop border control strategies that fully evaluate health risks if mandatory quarantines are lifted. In this study, we have employed a computational approach to investigate the contact tracing integrated policy in different border-reopening scenarios in Hong Kong, China. Explicitly, by reconstructing the COVID-19 transmission from historical data, specific scenarios with joint effects of digital contact tracing and other concurrent measures (i.e., controlling arrival population and community nonpharmacological interventions) are applied to forecast the future development of the pandemic. Built on a modified SEIR epidemic model with a 30% vaccination coverage, the results suggest that scenarios with digital contact tracing and quick isolation intervention can reduce the infectious population by 92.11% compared to those without contact tracing. By further restricting the inbound population with a 10,000 daily quota and applying moderate-to-strong community nonpharmacological interventions (NPIs), the average daily confirmed cases in the forecast period of 60 days can be well controlled at around 9 per day (95% CI: 7-12). Two main policy recommendations are drawn from the study. First, digital contact tracing would be an effective countermeasure for reducing local virus spread, especially when it is applied along with a moderate level of vaccination coverage. Second, implementing a daily quota on inbound travelers and restrictive community NPIs would further keep the local infection under control. This study offers scientific evidence and prospective guidance for developing and instituting plans to lift mandatory border control policies in preparing for the global economic recovery.

Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach.

BACKGROUND: Although by late February 2020 the COVID-19 epidemic was effectively controlled in Wuhan, China, estimating the effects of interventions, such as transportation restrictions and quarantine measures, on the early COVID-19 transmission dynamics in Wuhan is critical for guiding future virus containment strategies. Since the exact number of infected cases is unknown, the number of documented cases was used by many disease transmission models to infer epidemiological parameters. This means that it was possible to produce biased estimates of epidemiological parameters and hence of the effects of intervention measures, because the percentage of all cases that were documented changed during the first 2 months of the epidemic, as a consequence of a gradually improving diagnostic capability. METHODS: To overcome these limitations, we constructed a stochastic susceptible-exposed-infected-quarantined-recovered (SEIQR) model, accounting for intervention measures and temporal changes in the proportion of new documented infections out of total new infections, to characterize the transmission dynamics of COVID-19 in Wuhan across different stages of the outbreak. Pre-symptomatic transmission was taken into account in our model, and all epidemiological parameters were estimated using the Particle Markov-chain Monte Carlo (PMCMC) method. RESULTS: Our model captured the local Wuhan epidemic pattern as two-peak transmission dynamics, with one peak on February 4 and the other on February 12, 2020. The impact of intervention measures determined the timing of the first peak, leading to an 86% drop in the Re from 3.23 (95% CI, 2.22 to 4.20) to 0.45 (95% CI, 0.20 to 0.69). The improved diagnostic capability led to the second peak and a higher proportion of documented infections. Our estimated proportion of new documented infections out of the total new infections increased from 11% (95% CI 1-43%) to 28% (95% CI 4-62%) after January 26 when more detection kits were released. After the introduction of a new diagnostic criterion (case definition) on February 12, a higher proportion of daily infected cases were documented (49% (95% CI 7-79%)). CONCLUSIONS: Transportation restrictions and quarantine measures together in Wuhan were able to contain local epidemic growth.

Modeling COVID-19 Transmission Dynamics With Self-Learning Population Behavioral Change.

Many regions observed recurrent outbreaks of COVID-19 cases after relaxing social distancing measures. It suggests that maintaining sufficient social distancing is important for limiting the spread of COVID-19. The change of population behavior responding to the social distancing measures becomes an important factor for the pandemic prediction. In this paper, we develop a SEAIR model for studying the dynamics of COVID-19 transmission with population behavioral change. In our model, the population is divided into several groups with their own social behavior in response to the delayed information about the number of the infected population. The transmission rate depends on the behavioral changes of all the population groups, forming a feedback loop to affect the COVID-19 dynamics. Based on the data of Hong Kong, our simulations demonstrate how the perceived cost after infection and the information delay affect the level and the time period of the COVID-19 waves.