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Non-pharmaceutical interventions during the COVID-19 epidemic changed detection rates of other circulating respiratory pathogens in Japan.

ABSTRACT: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has circulated worldwide and causes coronavirus disease 2019 (COVID-19). At the onset of the COVID-19 pandemic, infection control measures were taken, such as hand washing, mask wearing, and behavioral restrictions. However, it is not fully clear how the effects of these non-pharmaceutical interventions changed the prevalence of other pathogens associated with respiratory infections. In this study, we collected 3,508 nasopharyngeal swab samples from 3,249 patients who visited the Yamanashi Central Hospital in Japan from March 1, 2020 to February 28, 2021. We performed multiplex polymerase chain reaction (PCR) using the FilmArray Respiratory Panel and singleplex quantitative reverse transcription PCR targeting SARS-CoV-2 to detect respiratory disease-associated pathogens. At least one pathogen was detected in 246 (7.0%) of the 3,508 samples. Eleven types of pathogens were detected in the samples collected from March-May 2020, during which non-pharmaceutical interventions were not well implemented. In contrast, after non-pharmaceutical interventions were thoroughly implemented, only five types of pathogens were detected, and the majority were SARS-CoV-2, adenoviruses, or human rhinoviruses / enteroviruses. The 0-9 year age group had a higher prevalence of infection with adenoviruses and human rhinoviruses / enteroviruses compared with those 10 years and older, while those 10 years and older had a higher prevalence of infection with SARS-CoV-2 and other pathogens. These results indicated that non-pharmaceutical interventions likely reduced the diversity of circulating pathogens. Moreover, differences in the prevalence of pathogens were observed among the different age groups.

SUBMITTER: Nagakubo Y

PROVIDER: S-EPMC8782330 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in young children worldwide. An annual epidemic of RSV infection generally begins around autumn, reaching a peak at the end of year in Japan, but in 2017 it started in early July and peaked in September. As the onset timing of RSV season varies, it is important to detect the beginning of an epidemic, to enable the implementation of preventive measures. However, there are currently no specified criteria or methods to determine the onset of RSV season in a timely manner. Therefore, we developed a model to detect the epidemic onset, based on data from the Infectious Diseases Weekly Report from 2012 to 2017. The 47 prefectures of Japan span 11 climate zones, which affect the timing of epidemic onset. Therefore, the onset of RSV season was assessed separately in each prefecture. Non-linear regression analysis was performed to generate a mathematical model of the annual epidemic cycle for each prefecture. A search index was used to determine the onset of RSV season, which was estimated using the number of RSV reports per week within an epidemic period (RSV-reports/w) and the number of reported cases included within an epidemic period relative to the total number of RSV reports (capture rate). A number of RSV-reports/w, which was used as a threshold (a number at onset line) to determine the condition of the onset of RSV season, was then estimated based on the search index. The mean number at the onset of RSV season for 47 prefectures was 29.7 reports/week (median 21.0, range 6.0-121.0 reports/ week). The model also showed that the onset of RSV season in 2017 was more than 1 month earlier than the previous year. In conclusion, the model detected epidemic cycles and their onset conditions in all prefectures, despite the 11 climate zones of Japan. The results are expected to contribute to infant medical care by allowing medical personnel to take preventive measures promptly at the beginning of the epidemic RSV season.

Project description:ObjectiveIn this study, we investigated the impact of COVID-19 NPIs in South Africa to understand their effectiveness in the reduction of transmission of COVID-19 in the South African population. This study also investigated the COVID-19 testing, reporting, hospitalised cases, excess deaths and COVID-19 modelling in the first wave of the COVID-19 epidemic in South Africa.MethodsA semi-reactive stochastic COVID-19 model, the ARI COVID-19 SEIR model, was used to investigate the impact of NPIs in South Africa to understand their effectiveness in the reduction of COVID-19 transmission in the South African population. COVID-19 testing, reporting, hospitalised cases and excess deaths in the first COVID-19 epidemic wave in South Africa were investigated using regressional analysis and descriptive statistics.FindingsThe general trend in population movement in South African locations shows that the COVID-19 NPIs (National Lockdown Alert Levels 5,4,3,2) were approximately 30% more effective in reducing population movement concerning each increase by 1 Alert Level. The translated reduction in the effective SARS-CoV-2 daily contact number (β) was 6.12% to 36.1% concerning increasing Alert Levels. Due to the implemented NPIs, the effective SARS-CoV-2 daily contact number in the first COVID-19 epidemic wave in South Africa was reduced by 58.1-71.1% while the peak was delayed by 84 days. The estimated COVID-19 reproductive number was between 1.98 to 0.40. During South Africa's first COVID-19 epidemic wave, the mean COVID-19 admission status in South African hospitals was 58.5%, 95% CI [58.1-59.0] in the general ward, 13.4%, 95% CI [13.1-13.7] in the intensive care unit, 13.3%, 95% CI [12.6-14.0] on oxygen, 6.37%, 95% CI [6.23-6.51] in high care, 6.29%, 95% CI [6.02-6.55] on ventilator and 2.13%, 95% CI [1.87-2.43] in isolation ward respectively. The estimated mean South African COVID-19 patient discharge rate was 11.9 days per patient. While the estimated mean of the South African COVID-19 patient case fatality rate (CFR) in hospital and outside the hospital was 2.06%, 95% CI [1.86-2.25] (deaths per admitted patients) and 2.30%, 95% CI [1.12-3.83](deaths per severe and critical cases) respectively. The relatively high coefficient of variance in COVID-19 model outputs observed in this study shows the uncertainty in the accuracy of the reviewed COVID-19 models in predicting the severity of COVID-19. However, the reviewed COVID-19 models were accurate in predicting the progression of the first COVID-19 epidemic wave in South Africa.ConclusionThe results from this study show that the COVID-19 NPI policies implemented by the Government of South Africa played a significant role in the reduction of COVID-19 active, hospitalised cases and deaths in South Africa's first COVID-19 epidemic wave. The results also show the use of COVID-19 modelling to understand the COVID-19 pandemic and the impact of regressor variables in an epidemic.

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Non-pharmaceutical interventions during the COVID-19 epidemic changed detection rates of other circulating respiratory pathogens in Japan.

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