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Objective: Vaccine-derived poliovirus (VDPV) remains a significant barrier to global poliomyelitis eradication. Following 18 years of polio-free status, an epidemic of circulating VDPV type 1 (cVDPV1) occurred in Papua New Guinea (PNG) in 2018. We describe the epidemiology of the 2018 cVDPV1 epidemic in PNG, and identify risk factors which may aid future policy and control efforts. Methods: Data pertaining to the 2018 PNG epidemic were extracted from EPIWATCH and supplemented with data from other sources, such as the World Health Organization (WHO), and published literature. Descriptive analyses were undertaken, and key risk factors identified. Results: 26 cases of cVDPV1 were confirmed throughout the duration of the epidemic (April to October 2018) in nine provinces. Of the 26 cases, 19 (73%) were males and 7 (27%) were females, and most of the cases (73%) occurred in children under the age of five. Population immunization coverage of three doses of oral polio vaccine (OPV3) was found to fluctuate between 60- 80% between 2000 and 2018. Nonpolio Acute Flaccid Paralysis (NPAFP) surveillance rates were also found to be suboptimal over this period. Discussion: A combination of low routine immunization coverage, lacking supplementary immunization activities, and ineffective surveillance systems, in the context of a struggling health system, culminated in this epidemic. To prevent future poliomyelitis epidemics in PNG, emphasis must be placed on supporting the health system to maintain high vaccination coverage, in conjunction with robust and effective surveillance systems.

An increase in the number of Acute Flaccid Myelitis (AFM) cases reported in the USA was detected in August 2018. The CDC has confirmed 186 cases of AFM in 2018 making this the largest outbreak of AFM reported in the USA. The cause however remains unknown. In this watching brief we review key features of the current outbreak in context, describe what is known about AFM, and consider probable causes. A two-year epidemic cycle of AFM appears to be emerging, and most current evidence implicates both EV-A71 and EV-D68 as the probable cause of the current outbreak yet key questions remain.

Hepatitis A (Hep A) can cause sporadic or epidemic disease and has been frequently linked to contamination of the global food chain. Global surveillance data on Hep A are unavailable, and in some countries, reporting is incomplete or not timely, either because of lack of human resources or sensitivities around reporting. The use of vast open-source data such as news-feeds and social media however can overcome barriers to surveillance and provide timely data on global epidemics. In this study we use EpiWATCH, semi-automated outbreak scanning service to review the global epidemiology of Hep A reports from 2016-2018. We reviewed the EpiWATCH Outbreak Alerts database for reports on the Hep A dated between August 1, 2016, to April 31, 2018 which was the analysed by outbreak clusters, location, and time. Of 5098 total entries in the database a total of 169 non-duplicate Hep A outbreak reports were found and included for descriptive analysis. The majority of outbreak reports (68.6%%; N=116/169) originated from the United States of America (USA). The largest Hep A outbreaks were multi-country outbreaks in the European region, and multistate outbreaks in the USA and Australia. Homelessness (mainly in US outbreaks) was the predominant risk factor (40.2%), followed by foodborne outbreaks (26.6%) and outbreaks in men who have sex with men (6.5%). Using EpiWATCH we found that the emergence of outbreaks in homeless people has dominated the epidemiology of Hep A in the U.S and this appears a relatively new phenomenon. Epidemic intelligence systems such as EpiWATCH are a useful proxy for global surveillance of Hep A outbreaks and using open-source data can provide epidemic intelligence and outbreak alerts where global data is unavailable.

Epidemics are influenced by both disease and societal factors and can grow exponentially over short time periods. Epidemic risk analysis can help in rapidly predicting potentially serious outcomes and flagging the need for rapid response. We developed a multifactorial risk analysis tool ‘EpiRisk’ to provide rapid insight into the potential severity of emerging epidemics by combining disease-related parameters and country-related risk parameters. An initial set of 18 disease and country-related risk parameters was reduced to 14 following qualitative discussions and the removal of highly correlated parameters by a correlation and clustering analysis. Of the remaining parameters, three risk levels were assigned ranging from low (1) moderate (2) and high (3). The total risk score for an outbreak of a given disease in a particular country is calculated by summing these 14 risk scores, and this sum is subsequently classified into one of four risk categories: low risk (37). Total risk scores were calculated for nine retrospective outbreaks demonstrating an association with the actual impact of those outbreaks. We also evaluated to what extent the risk scores correlate with the number of cases and deaths in 61 additional outbreaks between 2002 and 2018, demonstrating positive associations with outbreak severity as measured by the number of deaths. Using EpiRisk, timely intervention can be implemented by predicting the risk of emerging outbreaks in real time, which may help government and public health professionals prevent catastrophic epidemic outcomes.

Background: Hong Kong enforced stringent travel restrictions during the COVID-19 pandemic. Understanding the characteristics of imported COVID-19 cases is important for establishing evidence-based control measures. Methods: Retrospective cohort study summarising the characteristics of imported cases detected in Hong Kong between 13 November 2020 and 31 January 2022, when compulsory quarantine was implemented. Findings: A total of 2269 imported COVID-19 cases aged 0–85 years were identified, of which 48.6 % detected on arrival. A shorter median delay from arrival to isolation was observed in Delta and Omicron cases (3 days) than in ancestral strain and other variants cases (12 days; p

COVID-19 remains a persistent threat, especially with the predominant Omicron variant emerging in early 2022, presenting with high transmissibility, immune escape, and waning. There is a need to rapidly ramp up global vaccine coverage while enhancing public health and social measures. Timely and reliable estimation of the reproduction number throughout a pandemic is critical for assessing the impact of mitigation efforts and the potential need to adjust for control measures. We conducted a systematic review on the reproduction numbers of the Omicron variant and gave the pooled estimates. We identified six studies by searching PubMed, Embase, Web of Science, and Google Scholar for articles published between 1 January 2020 and 6 March 2022. We estimate that the effective reproduction number ranges from 2.43 to 5.11, with a pooled estimate of 4.20 (95% CI: 2.05, 6.35). The Omicron variant has an effective reproduction number which is triple (2.71 (95% CI: 1.86, 3.56)) that of the Delta variant.

Abstract Hong Kong experienced a surge of Omicron BA.2 infections in early 2022, resulting in one of the highest per-capita death rates of COVID-19. The outbreak occurred in a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures (PHSMs). By analyzing genome sequences and epidemiological data, we reconstructed the epidemic trajectory of BA.2 wave and found that the initial BA.2 community transmission emerged from cross-infection within hotel quarantine. The rapid implementation of PHSMs suppressed early epidemic growth but the effective reproduction number (R e ) increased again during the Spring festival in early February and remained around 1 until early April. Independent estimates of point prevalence and incidence using phylodynamics also showed extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. Discordant inferences based on genomic and epidemiological data underscore the need for research to improve near real-time epidemic growth estimates by combining multiple disparate data sources to better inform outbreak response policy.

The time-varying effective reproductive number (Rt) is useful for monitoring transmission of infections such as COVID-19, but reporting delays impact case count-based estimation methods. Here, the authors demonstrate and validate a method for estimation of Rt based on viral load data from Hong Kong that does not require accurate daily counts.

Hong Kong has used an elimination strategy to control SARS-CoV-2 with stringent measures including traveller quarantine. Here, the authors show that the majority of community-acquired cases until January 2021 resulted from three importations, and that increased transmission followed prolonged periods of restrictions, likely due to adherence fatigue.

Abstract Avian influenza H5N1 subtype has caused a global public health concern due to its high pathogenicity in poultry and high case fatality rates in humans. The recently emerged H7N9 is a growing pandemic risk due to its sustained high rates of human infections, and recently acquired high pathogenicity in poultry. Here, we used Bayesian phylogeography on 265 H5N1 and 371 H7N9 haemagglutinin sequences isolated from humans, animals and the environment, to identify and compare migration patterns and factors predictive of H5N1 and H7N9 diffusion rates in China. H7N9 diffusion dynamics and predictor contributions differ from H5N1. Key determinants of spatial diffusion included: proximity between locations (for H5N1 and H7N9), and lower rural population densities (H5N1 only). For H7N9, additional predictors included low avian influenza vaccination rates, low percentage of nature reserves and high humidity levels. For both H5N1 and H7N9, we found viral migration rates from Guangdong to Guangxi and Guangdong to Hunan were highly supported transmission routes (Bayes Factor > 30). We show fundamental differences in wide-scale transmission dynamics between H5N1 and H7N9. Importantly, this indicates that avian influenza initiatives designed to control H5N1 may not be sufficient for controlling the H7N9 epidemic. We suggest control and prevention activities to specifically target poultry transportation networks between Central, Pan-Pearl River Delta and South-West regions.

The clinical severity and observed case fatality ratio of influenza A/H1N1pdm09 in India, particularly in 2015 and 2017 far exceeds current global estimates. Reasons for these frequent and severe epidemic waves remain unclear. We used Bayesian phylodynamic methods to uncover possible genetic explanations for this, while also identifying the transmission dynamics of A/H1N1pdm09 between 2009 and 2017 to inform future public health interventions. We reveal a disproportionate selection at haemagglutinin residue positions associated with increased morbidity and mortality in India such as position 222 and clade 6B characteristic residues, relative to equivalent isolates circulating globally. We also identify for the first time, increased selection at position 186 as potentially explaining the severity of recent A/H1N1pdm09 epidemics in India. We reveal national routes of A/H1N1pdm09 transmission, identifying Maharashtra as the most important state for the spread throughout India, while quantifying climactic, ecological, and transport factors as drivers of within-country transmission. Together these results have important implications for future A/H1N1pdm09 surveillance and control within India, but also for epidemic and pandemic risk prediction around the world.