Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information

This paper addresses the challenge of sharing finer-scale Protected Health Information (PHI) while maintaining patient privacy by using regionalization to create higher resolution HIPAA-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, REDCAP, and self-organizing maps (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intra-unit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model-fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP which outperformed on most measures. The SOM variant of max-p-regions (MSOM) may also be recommended as it provided the highest resolution while maintaining suitable performance on all other measures.

Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey

BACKGROUND

Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks.

METHODS

We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated.

RESULTS

We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review.

CONCLUSIONS

Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study

BACKGROUND

The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide.

METHODS

Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country.

RESULTS

We found significant negative association between disease arrival time and number of cases imported from Italy (r = -0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre.

CONCLUSION

We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

Change in outbreak epicentre and its impact on the importation risks of COVID-19 progression: A modelling study

Background

The outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first detected in the city of Wuhan, China has now spread to every inhabitable continent, but now the attention has shifted from China to other epicentres. This study explored early assessment of the influence of spatial proximities and travel patterns from Italy on the further spread of SARS-CoV-2 worldwide.

Methods

Using data on the number of confirmed cases of COVID-19 and air travel data between countries, we applied a stochastic meta-population model to estimate the global spread of COVID-19. Pearson's correlation, semi-variogram, and Moran's Index were used to examine the association and spatial autocorrelation between the number of COVID-19 cases and travel influx (and arrival time) from the source country.

Results

We found significant negative association between disease arrival time and number of cases imported from Italy (r = −0.43, p = 0.004) and significant positive association between the number of COVID-19 cases and daily travel influx from Italy (r = 0.39, p = 0.011). Using bivariate Moran's Index analysis, we found evidence of spatial interaction between COVID-19 cases and travel influx (Moran's I = 0.340). Asia-Pacific region is at higher/extreme risk of disease importation from the Chinese epicentre, whereas the rest of Europe, South-America and Africa are more at risk from the Italian epicentre.

Conclusion

We showed that as the epicentre changes, the dynamics of SARS-CoV-2 spread change to reflect spatial proximities.

Analysis of COVID-19 Cases' Spatial Dependence in US Counties Reveals Health Inequalities

On March 13, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus SARS-CoV2 a pandemic. Since then the virus has infected over 9.1 million individuals and resulted in over 470,000 deaths worldwide (as of June 24, 2020). Here, we discuss the spatial correlation between county population health rankings and the incidence of COVID-19 cases and COVID-19 related deaths in the United States. We analyzed the spread of the disease based on multiple variables at the county level, using publicly available data on the numbers of confirmed cases and deaths, intensive care unit beds and socio-demographic, and healthcare resources in the U.S. Our results indicate substantial geographical variations in the distribution of COVID-19 cases and deaths across the US counties. There was significant positive global spatial correlation between the percentage of Black Americans and cases of COVID-19 (Moran I = 0.174 and 0.264, p < 0.0001). A similar result was found for the global spatial correlation between the percentage of Black American and deaths due to COVID-19 at the county level in the U.S. (Moran I = 0.264, p < 0.0001). There was no significant spatial correlation between the Hispanic population and COVID-19 cases and deaths; however, a higher percentage of non-Hispanic white was significantly negatively spatially correlated with cases (Moran I = -0.203, p < 0.0001) and deaths (Moran I = -0.137, p < 0.0001) from the disease. This study showed significant but weak spatial autocorrelation between the number of intensive care unit beds and COVID-19 cases (Moran I = 0.08, p < 0.0001) and deaths (Moran I = 0.15, p < 0.0001), respectively. These findings provide more detail into the interplay between the infectious disease and healthcare-related characteristics of the population. Only by understanding these relationships will it be possible to mitigate the rate of spread and severity of the disease.

Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran's I (Moran, 1950), Local Moran's I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran's I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran's I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).

Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

A System Based-Approach to Examine Host Response during Infection with Influenza A Virus Subtype H7N9 in Human and Avian Cells

Although the influenza A virus H7N9 subtype circulates within several avian species, it can also infect humans with a severe disease outcome. To better understand the biology of the H7N9 virus we examined the host response to infection in avian and human cells. In this study we used the A/Anhui/1/2013 strain, which was isolated during the first wave of the H7N9 epidemic. The H7N9 virus-infected both human (Airway Epithelial cells) and avian (Chick Embryo Fibroblast) cells, and each infected host transcriptome was examined with bioinformatic tools and compared with other representative avian and human influenza A virus subtypes. The H7N9 virus induced higher expression changes (differentially regulated genes) in both cell lines, with more prominent changes observed in avian cells. Ortholog mapping of differentially expression genes identified significant enriched common and cell-type pathways during H7N9 infections. This data confirmed our previous findings that different influenza A virus subtypes have virus-specific replication characteristics and anti-virus signaling in human and avian cells. In addition, we reported for the first time, the new HIPPO signaling pathway in avian cells, which we hypothesized to play a vital role to maintain the antiviral state of H7N9 virus-infected avian cells. This could explain the absence of disease symptoms in avian species that tested positive for the presence of H7N9 virus.

Virus-induced pathogenesis, vaccine development, and diagnosis of novel H7N9 avian influenza A virus in humans: a systemic literature review

H7N9 avian influenza virus (AIV) caused human infections in 2013 in China. Phylogenetic analyses indicate that H7N9 AIV is a novel reassortant strain with pandemic potential. We conducted a systemic review regarding virus-induced pathogenesis, vaccine development, and diagnosis of H7N9 AIV infection in humans. We followed PRISMA guidelines and searched PubMed, Web of Science, and Google Scholar to identify relevant articles published between January 2013 and December 2018. Pathogenesis data indicated that H7N9 AIV belongs to low pathogenic avian influenza, which is mostly asymptomatic in avian species; however, H7N9 induces high mortality in humans. Sporadic human infections have recently been reported, caused by highly pathogenic avian influenza viruses detected in poultry. H7N9 AIVs resistant to adamantine and oseltamivir cause severe human infection by rapidly inducing progressive acute community-acquired pneumonia, multiorgan dysfunction, and cytokine dysregulation; however, mechanisms via which the virus induces severe syndromes remain unclear. An H7N9 AIV vaccine is lacking; designs under evaluation include synthesized peptide, baculovirus-insect system, and virus-like particle vaccines. Molecular diagnosis of H7N9 AIVs is suggested over conventional assays, for biosafety reasons. Several advanced or modified diagnostic assays are under investigation and development. We summarized virus-induced pathogenesis, vaccine development, and current diagnostic assays in H7N9 AIVs.

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

Analysis of Epidemiological Characteristics of Notifiable Diseases Reported in Children Aged 0⁻14 Years from 2008 to 2017 in Zhejiang Province, China

This study aims to learn the characteristics of morbidity and mortality of notifiable diseases reported in children aged 0⁻14 years in Zhejiang Province in 2008⁻2017. We collated data from the China Information System for Disease Control and Prevention in Zhejiang province between 1 January 2008 and 31 December 2017 of children aged 0⁻14 years. From 2008 to 2017, a total of 32 types and 1,994,740 cases of notifiable diseases were reported in children aged 0⁻14 years, including 266 deaths in Zhejiang Province. The annual average morbidity was 2502.87/100,000, and the annual average mortality was 0.33/100,000. Male morbidity was 2886.98/100,000, and female morbidity was 2072.16/100,000, with the male morbidity rate higher than the female morbidity rate (χ² = 54,033.12, p < 0.01). No Class A infectious diseases were reported. The morbidity of Class B infectious diseases showed a downward trend, but that of Class C infectious diseases showed an upward trend. There were 72,041 cases in 22 kinds of Class B infectious disease and 138 death cases, with a morbidity rate of 90.39/100,000, and a mortality rate of 0.17/100,000. There were 1,922,699 cases in 10 kinds of Class C infectious disease and 128 death cases, with a morbidity rate of 2412.47/100,000, and a mortality rate of 0.16/100,000. The main high-prevalence diseases included hand-foot-and-mouth disease (1430.38/100,000), other infectious diarrheal diseases (721.40/100,000), mumps (168.83/100,000), and influenza (47.40/100,000). We should focus on the prevention and control of hand-foot and mouth disease, other infectious diarrheal diseases, mumps and influenza in children aged 0⁻14 years in Zhejiang Province. It is recommended to strengthen epidemic surveillance and undertake early prevention and control measures in order to reduce the younger children incidence rate of infectious diseases. Immunization planning vaccines can help achieve a significant preventive decline of infectious diseases.

Assessment of low pathogenic avian influenza virus transmission via raw poultry meat and raw table eggs

A rapid qualitative assessment has been done by performing a theoretical analysis on the transmission of low pathogenic avian influenza (LPAI) via fresh meat from poultry reared or kept in captivity for the production of meat (raw poultry meat) or raw table eggs. A predetermined transmission pathway followed a number of steps from a commercial or non-commercial poultry establishment within the EU exposed to LPAI virus (LPAIV) to the onward virus transmission to animals and humans. The combined probability of exposure and subsequent LPAIV infection via raw poultry meat containing LPAIV is negligible for commercial poultry and humans exposed via consumption whereas it is very unlikely for non-commercial poultry, wild birds and humans exposed via handling and manipulation. The probability of LPAIV transmission from an individual infected via raw poultry meat containing LPAIV is negligible for commercial poultry and humans, whereas it is very unlikely for non-commercial poultry and wild birds. The combined probability of exposure and subsequent LPAIV infection via raw table eggs containing LPAIV is negligible for commercial poultry and humans and extremely unlikely to negligible for non-commercial poultry and wild birds. The probability of LPAIV transmission from an individual infected via raw table eggs containing LPAIV is negligible for commercial poultry and humans and very unlikely to negligible for non-commercial poultry and wild birds. Although the presence of LPAIV in raw poultry meat and table eggs is very unlikely to negligible, there is in general a high level of uncertainty on the estimation of the subsequent probabilities of key steps of the transmission pathways for poultry and wild birds, mainly due to the limited number of studies available, for instance on the viral load required to infect a bird via raw poultry meat or raw table eggs containing LPAIV.

Spatio-temporal pattern analysis for evaluation of the spread of human infections with avian influenza A(H7N9) virus in China, 2013-2014

BACKGROUND

A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014.

METHODS

Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans.

RESULTS

The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III.

CONCLUSIONS

There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China.

Emergence and development of H7N9 influenza viruses in China

The occurrence of human infections with avian H7N9 viruses since 2013 demonstrates the continuing pandemic threat posed by the current influenza ecosystem in China. Influenza surveillance and phylogenetic analyses showed that these viruses were generated by multiple interspecies transmissions and reassortments among the viruses resident in domestic ducks and the H9N2 viruses enzootic in chickens. A large population of domestic ducks hosting diverse influenza viruses provided the precondition for these events to occur, while acquiring internal genes from enzootic H9N2 influenza viruses in chickens promoted the spread of these viruses. Human infections effectively act as sentinels, reflecting the intensity of the activity of these viruses in poultry.

The pandemic potential of avian influenza A(H7N9) virus: a review

In March 2013 the first cases of human avian influenza A(H7N9) were reported to the World Health Organization. Since that time, over 650 cases have been reported. Infections are associated with considerable morbidity and mortality, particularly within certain demographic groups. This rapid increase in cases over a brief time period is alarming and has raised concerns about the pandemic potential of the H7N9 virus. Three major factors influence the pandemic potential of an influenza virus: (1) its ability to cause human disease, (2) the immunity of the population to the virus, and (3) the transmission potential of the virus. This paper reviews what is currently known about each of these factors with respect to avian influenza A(H7N9). Currently, sustained human-to-human transmission of H7N9 has not been reported; however, population immunity to the virus is considered very low, and the virus has significant ability to cause human disease. Several statistical and geographical modelling studies have estimated and predicted the spread of the H7N9 virus in humans and avian species, and some have identified potential risk factors associated with disease transmission. Additionally, assessment tools have been developed to evaluate the pandemic potential of H7N9 and other influenza viruses. These tools could also hypothetically be used to monitor changes in the pandemic potential of a particular virus over time.

Changing risk awareness and personal protection measures for low to high pathogenic avian influenza in live-poultry markets in Taiwan, 2007 to 2012

Background

Outbreaks of low and high pathogenic avian influenza (LPAI, HPAI) H5N2 in chickens have occurred in Taiwan since 2003 and 2012, respectively. Fully understanding the different awareness, attitudes and protective behaviors adopted by workers in live-poultry markets (LPMWs) and local community residents (CRs) to face the challenges of LPAI and HPAI is very important to minimize viral adaptations to human populations.

Methods

A structural questionnaire containing information on respondents’ occupation, personal risk awareness, attitudes toward different policies, and preventative measures was administered. The two-stage survey (before and after HPAI H5N2 outbreaks) was conducted from 2007 to 2012, including: (1) 430 LPMWs and 418 CRs at LPMs from different geographical areas of Taiwan after the government announced outbreaks of LPAI H5N2 during 2007–2009, and (2) 73 LPMWs and 152 CRs at two LPMs in central Taiwan after the HPAI H5N2 outbreaks in 2012. The chi-squared test and logistic regression were applied for univariate and multivariate analyses, respectively.

Results

Before HPAI-H5N2 outbreaks, higher educated respondents demonstrated greater risk awareness and concerns regarding AI. However, LPM-workers protected themselves less from AI viruses (AIVs) and had lower acceptance of human or avian influenza vaccines. Most importantly, the participants who opposed (versus agreed with) the policy on banning live-poultry slaughtering at LPMs reported lower awareness of government prevention and control policies [Odds Ratio (OR): 0.76, 95 % Confidence Interval (CI): 0.56–1.01] or practiced preventive measures (OR: 0.42, 95 % CI: 0.25–0.70).

After HPAI-H5N2 outbreaks, the risk awareness about AI in central Taiwan significantly increased [LPAI to HPAI LPMWs: 34.6 to 65.6 %, p < 0.05; CRs: 44.0 to 76.5 %, p < 0.05] and LPMWs’ belief in the effectiveness of vaccination to prevent human or avian influenza virus infection strikingly decreased (92.3 to 68.5 %, p < 0.05).

Conclusions

Risk awareness depends on high or low pathogenicity of AIVs, working in LPMs, levels of education, age, and proximity to the sites of severe AI outbreaks. Regardless of novel LPAI or HPAI virus reassortants that pose public health risks, prompt and clear risk communication focusing on both correct information about AIVs and the most appropriate preventive measures are important for effective prevention of human infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-0987-8) contains supplementary material, which is available to authorized users.

Age and gender adjusted comparison of clinical features between severe cases infected with H7N9 and H1N1pdm influenza A in Jiangsu Province, China

Background

Influenza H7N9 and H1N1pdm can cause severe human infections. It is important to investigate the distinguishing clinical features between these two diseases. Several studies have compared the differences in general, however, age and gender adjusted comparisons may be more useful and informative to the health professionals.

Methods

A total of 184 severe H1N1pdm patients and 37 severe H7N9 patients from Jiangsu Province were included in this analysis to perform age and gender adjusted comparison of clinical features.

Results

After adjusting age and gender, no significant differences in chronic medical conditions or treatment were found between severely ill patients with H7N9 and H1N1pdm. Severely ill patients with H7N9 had significantly longer interval from onset of illness to neuraminidase inhibitor treatment and to death. They were more likely to have complications such as acute respiratory distress syndrome (ARDS), liver and renal dysfunctions, and had a significantly higher risk of death.

Conclusion

Our results suggests that age and gender should be adjusted as important confounding factors when comparing the clinical features between severe H7N9 and H1N1pdm patients to avoid any misunderstanding regarding the differences between these two diseases particularly in terms of clinical severity and prognosis.

An overview of the characteristics of the novel avian influenza A H7N9 virus in humans

The novel avian influenza A H7N9 virus which caused the first human infection in Shanghai, China; was reported on the 31st of March 2013 before spreading rapidly to other Chinese provinces and municipal cities. This is the first time the low pathogenic avian influenza A virus has caused human infections and deaths; with cases of severe respiratory disease with pneumonia being reported. There were 440 confirmed cases with 122 fatalities by 16 May 2014; with a fatality risk of ∼28%. The median age of patients was 61 years with a male-to-female ratio of 2.4:1. The main source of infection was identified as exposure to poultry and there is so far no definitive evidence of sustained person-to-person transmission. The neuraminidase inhibitors, namely oseltamivir, zanamivir, and peramivir; have shown good efficacy in the management of the novel H7N9 virus. Treatment is recommended for all hospitalized patients, and for confirmed and probable outpatient cases; and should ideally be initiated within 48 h of the onset of illness for the best outcome. Phylogenetic analysis found that the novel H7N9 virus is avian in origin and evolved from multiple reassortments of at least four origins. Indeed the novel H7N9 virus acquired human adaptation via mutations in its eight RNA gene segments. Enhanced surveillance and effective global control are essential to prevent pandemic outbreaks of the novel H7N9 virus.

Continuing reassortment leads to the genetic diversity of influenza virus H7N9 in Guangdong, China

On 30 March 2013, a novel avian influenza A H7N9 virus causing severe human respiratory infections was identified in China. Preliminary sequence analyses have shown that the virus is a reassortant of H7N9 and H9N2 avian influenza viruses. In this study, we conducted enhanced surveillance for H7N9 virus in Guangdong, China, from April to August 2013. We isolated two H7N9 viral strains from environmental samples associated with poultry markets and one from a clinical patient. Sequence analyses showed that the Guangdong H7N9 virus isolated from April to May shared high sequence similarity with other strains from eastern China. The A/Guangdong/1/2013 (H7N9) virus isolated from the Guangdong patient on 10 August 2013 was divergent from previously sequenced H7N9 viruses and more closely related to local circulating H9N2 viruses in the NS and NP genes. Phylogenetic analyses revealed that four internal genes of the A/Guangdong/1/2013 (H7N9) virus-the NS, NP, PB1, and PB2 genes-were in clusters different from those for H7N9 viruses identified previously in other provinces of China. The discovery presented here suggests that continuing reassortment led to the emergence of the A/Guangdong/1/2013 (H7N9) virus as a novel H7N9 virus in Guangdong, China, and that viral adaptation to avian and human hosts must be assessed.

IMPORTANCE

In this study, we isolated and characterized the avian influenza A H7N9 virus in Guangdong, China, from April to August 2013. We show that the viruses isolated from Guangdong environmental samples and chickens from April to May 2013 were highly similar to other H7N9 strains found in eastern China. The H7N9 virus isolated from the clinical patient in Guangdong in August 2013 was divergent from previously identified H7N9 viruses, with the NS and NP genes originating from recent H9N2 viruses circulating in the province. This study provides direct evidence that continuing reassortment occurred and led to the emergence of a novel H7N9 influenza virus in Guangdong, China. These results also shed light on how the H7N9 virus evolved, which is critically important for future monitoring and tracing of viral transmission.