Chronological, geographical, and seasonal trends of human cases of avian influenza A (H5N1) in Vietnam, 2003-2014: a spatial analysis

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

BACKGROUND

Several models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves.

METHODS

We used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the "rising trend line"; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave.

RESULTS

Up to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy.

CONCLUSION

Our newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Climate change and human health in Vietnam: a systematic review and additional analyses on current impacts, future risk, and adaptation

This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs. Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy. Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss. Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources.

Detection of space–time clusters using a topological hierarchy for geospatial data on COVID-19 in Japan

In this paper, we detected space–time clusters using data on coronavirus disease 2019 (COVID-19) collected daily by each prefecture in Japan. COVID-19 has spread globally since the first confirmed case in China, in December 2019. Several people have to date been infected in Japan since the first confirmed case in January 2020. The outbreak of COVID-19 has had a significant impact on many people’s lives. Studies are being conducted to detect regions, called clusters, which pose a significantly higher risk of infection than their surrounding areas, based on a spatial scan statistics of COVID-19 infections. Among these studies, space–time cluster detection has to date been actively performed to gain knowledge regarding infection status. Based on the spatial scan statistic, the cylindrical scan method is a widely used space–time cluster detection method. This method enables concurrent detection of the location and time of a cluster occurrence. However, this method cannot capture spatial changes in a cluster over time. When applying the existing method to a cluster whose shape changes over time, the number of calculations required becomes extremely large, and the analysis may become difficult. In this study, we focused on the hierarchical structure of the data obtained by conducting an echelon analysis and applied the space–time cluster detection method based on this structure to enable the capture of changes in a cluster’s shape. Furthermore, we visualized the location and period of a cluster’s occurrence and considered the cause of the cluster.

Spatiotemporal distribution of COVID-19 during the first 7 months of the epidemic in Vietnam

Background

Understanding the spatiotemporal distribution of emerging infectious diseases is crucial for implementation of control measures. In the first 7 months from the occurrence of COVID-19 pandemic, Vietnam has documented comparatively few cases of COVID-19. Understanding the spatiotemporal distribution of these cases may contribute to development of global countermeasures.

Methods

We assessed the spatiotemporal distribution of COVID-19 from 23 January to 31 July 2020 in Vietnam. Data were collected from reports of the World Health Organization, the Vietnam Ministry of Health, and related websites. Temporal distribution was assessed via the transmission classification (local or quarantined cases). Geographical distribution was assessed via the number of cases in each province along with their timelines. The most likely disease clusters with elevated incidence were assessed via calculation of the relative risk (RR).

Results

Among 544 observed cases of COVID-19, the median age was 35 years, 54.8% were men, and 50.9% were diagnosed during quarantine. During the observation period, there were four phases: Phase 1, COVID-19 cases occurred sporadically in January and February 2020; Phase 2, an epidemic wave occurred from the 1st week of March to the middle of April (Wave 1); Phase 3, only quarantining cases were involved; and Phase 4, a second epidemic wave began on July 25th, 2020 (Wave 2). A spatial cluster in Phase 1 was detected in Vinh Phuc Province (RR, 38.052). In Phase 2, primary spatial clusters were identified in the areas of Hanoi and Ha Nam Province (RR, 6.357). In Phase 4, a spatial cluster was detected in Da Nang, a popular coastal tourist destination (RR, 70.401).

Conclusions

Spatial disease clustering of COVID-19 in Vietnam was associated with large cities, tourist destinations, people’s mobility, and the occurrence of nosocomial infections. Past experiences with outbreaks of emerging infectious diseases led to quick implementation of governmental countermeasures against COVID-19 and a general acceptance of these measures by the population. The behaviors of the population and the government, as well as the country’s age distribution, may have contributed to the low incidence and small number of severe COVID-19 cases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06822-0.

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.

Novel reassortant H5N6 highly pathogenic influenza A viruses in Vietnamese quail outbreaks

Avian influenza A H5N6 virus is a highly contagious infectious agent that affects domestic poultry and humans in South Asian countries. Vietnam may be an evolutionary hotspot for influenza viruses and therefore could serve as a source of pandemic strains. In 2015, two novel reassortant H5N6 influenza viruses designated as A/quail/Vietnam/CVVI01/2015 and A/quail/Vietnam/CVVI03/2015 were isolated from dead quails during avian influenza outbreaks in central Vietnam, and the whole genome sequences were analyzed. The genetic analysis indicated that hemagglutinin, neuraminidase, and polymerase basic protein 2 genes of the two H5N6 viruses are most closely related to an H5N2 virus (A/chicken/Zhejiang/727079/2014) and H10N6 virus (A/chicken/Jiangxi/12782/2014) from China and an H6N6 virus (A/duck/Yamagata/061004/2014) from Japan. The HA gene of the isolates belongs to clade 2.3.4.4, which caused human fatalities in China during 2014-2016. The five other internal genes showed high identity to an H5N2 virus (A/chicken/Heilongjiang/S7/2014) from China. A whole-genome phylogenetic analysis revealed that these two outbreak strains are novel H6N6-like PB2 gene reassortants that are most closely related to influenza virus strain A/environment/Guangdong/ZS558/2015, which was detected in a live poultry market in China. This report describes the first detection of novel H5N6 reassortants in poultry during an outbreak as well as genetic characterization of these strains to better understand the antigenic evolution of influenza viruses.

Internal genes of a highly pathogenic H5N1 influenza virus determine high viral replication in myeloid cells and severe outcome of infection in mice

The highly pathogenic avian influenza (HPAI) H5N1 influenza virus has been a public health concern for more than a decade because of its frequent zoonoses and the high case fatality rate associated with human infections. Severe disease following H5N1 influenza infection is often associated with dysregulated host innate immune response also known as cytokine storm but the virological and cellular basis of these responses has not been clearly described. We rescued a series of 6:2 reassortant viruses that combined a PR8 HA/NA pairing with the internal gene segments from human adapted H1N1, H3N2, or avian H5N1 viruses and found that mice infected with the virus with H5N1 internal genes suffered severe weight loss associated with increased lung cytokines but not high viral load. This phenotype did not map to the NS gene segment, and NS1 protein of H5N1 virus functioned as a type I IFN antagonist as efficient as NS1 of H1N1 or H3N2 viruses. Instead we discovered that the internal genes of H5N1 virus supported a much higher level of replication of viral RNAs in myeloid cells in vitro, but not in epithelial cells and that this was associated with high induction of type I IFN in myeloid cells. We also found that in vivo during H5N1 recombinant virus infection cells of haematopoetic origin were infected and produced type I IFN and proinflammatory cytokines. Taken together our data infer that human and avian influenza viruses are differently controlled by host factors in alternative cell types; internal gene segments of avian H5N1 virus uniquely drove high viral replication in myeloid cells, which triggered an excessive cytokine production, resulting in severe immunopathology.

Some avian influenza viruses, including highly pathogenic H5N1 virus, cause severe disease in humans and in experimental animal models associated with excessive cytokine production. We aimed to understand the virological mechanism behind the cytokine storm, and particularly the contribution of internal gene segments that encode the viral polymerase and the non-structural proteins, since these might be retained in a pandemic virus. We found that the internal genes from an H5N1 avian influenza virus allowed virus to replicate to strikingly higher levels in myeloid cells compared to internal genes of human adapted strains. The higher viral RNA levels did not lead to higher viral load but drove excessive cytokine production and more severe outcome in infected mice. The remarkable difference in viral replication in myeloid cells was not observed in lung epithelial cells, suggesting that cell type specific differences in host factors were responsible. Understanding the molecular basis of excessive viral replication in myeloid cells may guide future therapeutic options for viruses that have recently crossed into humans from birds.

Surveillance of Live Poultry Markets for Low Pathogenic Avian Influenza Viruses in Guangxi Province, Southern China, from 2012-2015

Infections with low pathogenic avian influenza viruses (LPAIVs) can be mild or asymptomatic in poultry; however, in humans, LPAIVs can cause severe infections and death, as demonstrated by the H7N9 and H10N8 human infection outbreaks in 2013 in China. In this study, we conducted an epidemiological survey of LPAIVs at live poultry markets (LPMs) in Guangxi Province, Southern China, which is near several Southeast Asian countries. From January 2012 to December 2015, we collected 3,813 swab samples from poultry at LPMs in Guangxi. Viral isolation, hemagglutination inhibition assay and viral sequencing were utilized to identify LPAIVs in the collected samples. Among the samples, 622 (16.3%) were positive for LPAIVs. Six subtypes (H1, H3, H4, H6, H9 and H11) were individually isolated and identified. Of these subtypes, H3, H6 and H9 were predominant in ducks, geese and chickens, respectively. Among the 622 positive samples, 160 (25.7%) contained more than one subtype, and H8, H10, H12, H13, and H16 were identified among them, which highlights the continuous need for enhanced surveillance of AIVs. These results provide detailed information regarding the epidemic situation of LPAIVs in the area, which can aid efforts to prevent and control AIV transmission in humans and animals.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.