Global patterns of rebound to normal RSV dynamics following COVID-19 suppression.

BACKGROUND: Annual epidemics of respiratory syncytial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting in April 2020, RSV seasonal activity declined due to COVID-19 non-pharmaceutical interventions (NPIs) before re-emerging after relaxation of NPIs. We described the unusual patterns of RSV epidemics that occurred in multiple subsequent waves following COVID-19 in different countries and explored factors associated with these patterns. METHODS: Weekly cases of RSV from twenty-eight countries were obtained from the World Health Organisation and combined with data on country-level characteristics and the stringency of the COVID-19 response. Dynamic time warping and regression were used to cluster time series patterns and describe epidemic characteristics before and after COVID-19 pandemic, and identify related factors. RESULTS: While the first wave of RSV epidemics following pandemic suppression exhibited unusual patterns, the second and third waves more closely resembled typical RSV patterns in many countries. Post-pandemic RSV patterns differed in their intensity and/or timing, with several broad patterns across the countries. The onset and peak timings of the first and second waves of RSV epidemics following COVID-19 suppression were earlier in the Southern than Northern Hemisphere. The second wave of RSV epidemics was also earlier with higher population density, and delayed if the intensity of the first wave was higher. More stringent NPIs were associated with lower RSV growth rate and intensity and a shorter gap between the first and second waves. CONCLUSION: Patterns of RSV activity have largely returned to normal following successive waves in the post-pandemic era. Onset and peak timings of future epidemics following disruption of normal RSV dynamics need close monitoring to inform the delivery of preventive and control measures.

At the height of the storm: Healthcare staff's health conditions and job satisfaction and their associated predictors during the epidemic peak of COVID-19.

This study reports the physical health, mental health, anxiety, depression, distress, and job satisfaction of healthcare staff in Iran when the country faced its highest number of total active COVID-19 cases. In a sample of 304 healthcare staff (doctors, nurses, radiologists, technicians, etc.), we found a sizable portion reached the cutoff levels of disorders in anxiety (28.0%), depression (30.6%), and distress (20.1%). Age, gender, education, access to PPE (personal protective equipment), healthcare institutions (public vs. private), and individual status of COVID-19 infection each predicted some but not all the outcome variables of SF-12, PHQ-4, K6, and job satisfaction. The healthcare workers varied greatly in their access to PPE and in their status of COVID-19 infection: negative (69.7%), unsure (28.0%), and positive (2.3%). The predictors were also different from those identified in previous studies of healthcare staff during the COVID-19 crisis in China. This study helps to identify the healthcare staff in need to enable more targeted help as healthcare staff in many countries are facing peaks in their COVID-19 cases.

Seasonality of newly notified pulmonary tuberculosis in Japan, 2007-2015.

BACKGROUND: The seasonality of pulmonary tuberculosis (TB) incidence may indicate season-specific risk factors that could be controlled if they were better understood. The aims of this study were to elucidate how the incidence of TB changes seasonally and to determine the factors influencing TB incidence, to reduce the TB burden in Japan. METHODS: We assessed the seasonality of newly notified TB cases in Japan using national surveillance data collected between 2007 and 2015. To investigate age and sex differences, seasonal variation was analyzed according to sex for all cases and then by stratified age groups (0-4, 5-14, 15-24, 25-44, 45-64, 65-74, and ≥ 75 years). We used Roger's test to analyze the cyclic monthly trends in seasonal variation of TB incidence. RESULTS: A total of 199,856 newly notified TB cases (male, 62.2%) were reported over the past 9-year period. Among them, 60.6% involved patients aged ≥65 years. Overall, the peak months of TB incidence occurred from April to October, excluding September. In the analysis stratified by age group, a significant seasonal variation in TB cases was observed for age groups ≥15 years, whereas no seasonal variation was observed for age groups ≤14 years. For female patients aged ≥25 years, the peak TB epidemic period was seen from June to December, excluding November. Male patients in the same age groups exhibited declining TB incidence from September to March. CONCLUSIONS: TB incidence exhibits seasonality in Japan for people aged > 15 years and peaks in summer to fall. Monthly trends differ according to age and sex. For age groups ≥25 years, cases in women showed longer peaks from June to December whereas cases in men declined from September to December. These results suggest that the seasonality of TB incidence in Japan might be influenced by health checkups in young adults, reactivation of latent TB infection with aging, and lifestyle habits in older adults. These findings can contribute to establishing the potential determinants of TB seasonality in Japan.

Important changes in the timing of influenza epidemics in the WHO European Region over the past 20 years: virological surveillance 1996 to 2016.

IntroductionThe global epidemiology of many infectious diseases is changing, but little attention has been paid to whether the timing of seasonal influenza epidemics changed in recent years. This study investigated whether the timing of the peak of influenza epidemics has changed in countries of the World Health Organization (WHO) European Region between 1996 and 2016. Methods: Surveillance data were obtained from the WHO FluNet database. For each country and season (July to June of the next year), the peak was defined as the week with the highest 3-week moving average for reported cases. Linear regression models were used to test for temporal trends in the timing of the epidemic peak in each country and to determine whether this differed geographically. Results: More than 600,000 influenza cases were included from 38 countries of the WHO European Region. The timing of the epidemic peak changed according to a longitudinal gradient, occurring progressively later in Western Europe (e.g. by 2.8 days/season in Spain) and progressively earlier in Eastern Europe (e.g. by 3.5 days/season in the Russian Federation). Discussion: These results were confirmed in several sensitivity analyses. Our findings have implications for influenza control and prevention measures in the WHO European Region, for instance for the implementation of influenza vaccination campaigns.

Forecast of peak infection and estimate of excess deaths in COVID-19 transmission and prevalence in Taiyuan City, 2022 to 2023.

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures.

Preventive Behaviors and Influencing Factors among Thai Residents in Endemic Areas during the Highest Epidemic Peak of the COVID-19 Outbreak.

This research aims to investigate COVID-19 preventive behavior and influencing factors among Thai residents during the highest epidemic peak of COVID-19. Nine hundred and forty-six residents in five districts with high COVID-19 infection cases in Thailand were systematically included in this cross-sectional survey. The results showed that 87.2% and 65.2% of the residents had a high level of general knowledge and preventive measures, respectively. As to COVID-19 attitudes, poor levels of attitude among Thai residents were found in risk perception (53.6%) and mistrust issues (70.4%). Moreover, this study presents good preventive behavior (77.0%) among Thai residents. Multiple logistic regression showed that the influence factors of COVID-19 preventive behavior were the young age group (AOR 2.97, 95% CI 1.68-5.25), high income (AOR 1.38, 95% CI 1.03-1.86), and high level of general COVID-19 knowledge (AOR 2.21, 95% CI 1.64-2.96). The conclusion was that providing information on COVID-19 via social media was the key mechanism of policy action for increasing the level of COVID-19 preventive behavior during the highest epidemic peak in Thailand. In addition, the pandemic preparedness and response policy, with resident participation and involvement, could be recommended for the resilience of pandemic preparedness.

Transmission parameters of coronavirus disease 2019 in South Asian countries.

OBJECTIVES: This study aimed to estimate the transmission parameters, effective reproduction number, epidemic peak, and future exposure of coronavirus disease 2019 (COVID-19) in South Asian countries. METHODS: A susceptible-exposed-infected-recovered-death (SEIRD) model programmed with MATLAB was developed for this purpose. Data were collected (till June 28, 2021) from the official webpage of World Health Organization, along with the Center for Systems Science and Engineering at Johns Hopkins University. The model was simulated to measure the primary transmission parameters. The reproduction number was measured using the next-generating matrix method. RESULTS: The primary transmission rate followed an exponential Gaussian process regression. India showed the highest transmission rate (0.037) and Bhutan the lowest (0.023). The simulated epidemic peaks matched the reported peaks, thereby validating the SEIRD model. The simulation was carried out up to December 31, 2020 using the reported data till June 9, 2020. CONCLUSION: The information gathered in this research will be helpful for authorities to prevent the transmission of COVID-19 in the subsequent wave or in the future.

Global patterns of seasonal influenza activity, duration of activity and virus (sub)type circulation from 2010 to 2020.

BACKGROUND: Seasonal influenza viruses undergo unpredictable changes, which may lead to antigenic mismatch between circulating and vaccine strains and to a reduced vaccine effectiveness. A continuously updated knowledge of influenza strain circulation and seasonality is essential to optimize the effectiveness of influenza vaccination campaigns. We described the global epidemiology of influenza between the 2009 A(H1N1)p and the 2020 COVID-19 pandemic. METHODS: Influenza virological surveillance data were obtained from the WHO-FluNet database. We determined the median proportion of influenza cases caused by the different influenza virus types, subtypes, and lineages; the typical timing of the epidemic peak; and the median duration of influenza epidemics (applying the annual average percentage method with a 75% threshold). RESULTS: We included over 4.6 million influenza cases from 149 countries. The median proportion of influenza cases caused by type A viruses was 75.5%, highest in the Southern hemisphere (81.6%) and lowest in the intertropical belt (73.0%), and ranged across seasons between 60.9% in 2017 and 88.7% in 2018. Epidemic peaks typically occurred during winter months in Northern and Southern hemisphere countries, while much more variability emerged in tropical countries. Influenza epidemics lasted a median of 25 weeks (range 8-42) in countries lying between 30°N and 26°S, and a median of 9 weeks (range 5-25) in countries outside this latitude range. CONCLUSIONS: This work will establish an important baseline to better understand factors that influence seasonal influenza dynamics and how COVID-19 may have affected seasonal activity and influenza virus types, subtypes, and lineages circulation patterns.

Predicting intervention effect for COVID-19 in Japan: state space modeling approach.

Japan has observed a surge in the number of confirmed cases of the coronavirus disease (COVID-19) that has caused a serious impact on the society especially after the declaration of the state of emergency on April 7, 2020. This study analyzes the real time data from March 1 to April 22, 2020 by adopting a sophisticated statistical modeling based on the state space model combined with the well-known susceptible-infected-recovered (SIR) model. The model estimation and forecasting are conducted using the Bayesian methodology. The present study provides the parameter estimates of the unknown parameters that critically determine the epidemic process derived from the SIR model and prediction of the future transition of the infectious proportion including the size and timing of the epidemic peak with the prediction intervals that naturally accounts for the uncertainty. Even though the epidemic appears to be settling down during this intervention period, the prediction results under various scenarios using the data up to May 18 reveal that the temporary reduction in the infection rate would still result in a delayed the epidemic peak unless the long-term reproduction number is controlled.

Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020.

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible-Exposed-Infectious-Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July-11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Moving Average Based Index for Judging the Peak of the COVID-19 Epidemic.

A pneumonia outbreak caused by a novel coronavirus (COVID-19) has spread around the world. A total of 2,314,621 laboratory-confirmed cases, including 157,847 deaths (6.8%) were reported globally by 20 April 2020. Common symptoms of COVID-19 pneumonia include fever, fatigue, and dry cough. Faced with such a sudden outbreak of emerging infectious disease, traditional models for predicting the peak of the epidemic often show inconsistent results. With the aim to timely judge the epidemic peak and provide support for decisions for resuming production and returning to normal life based on publicly reported data, we used a seven-day moving average of log-transformed daily new cases (LMA) to establish a new index named the "epidemic evaluation index" (EEI). We used SARS epidemic data from Hong Kong to verify the practicability of the new index, and then applied it to the COVID-19 epidemic analysis. The results showed that the epidemic peaked, respectively, on 9 February and 5 February 2020, in Hubei Province and other provinces in China. The proposed index can be applied for judging the epidemic peak. While the global COVID-19 epidemic reached its peak in the middle of April, the epidemic peaks in some countries have not yet appeared. Global and united efforts are still needed to eventually eliminate the epidemic.