New variants and age shift to high fatality groups contribute to severe successive waves in the 2009 influenza pandemic in Taiwan

Past influenza pandemics have been characterized by the signature feature of multiple waves. However, the reasons for multiple waves in a pandemic are not understood. Successive waves in the 2009 influenza pandemic, with a sharp increase in hospitalized and fatal cases, occurred in Taiwan during the winter of 2010. In this study, we sought to discover possible contributors to the multiple waves in this influenza pandemic. We conducted a large-scale analysis of 4703 isolates in an unbiased manner to monitor the emergence, dominance and replacement of various variants. Based on the data from influenza surveillance and epidemic curves of each variant clade, we defined virologically and temporally distinct waves of the 2009 pandemic in Taiwan from May 2009 to April 2011 as waves 1 and 2, an interwave period and wave 3. Except for wave 3, each wave was dominated by one distinct variant. In wave 3, three variants emerged and co-circulated, and formed distinct phylogenetic clades, based on the hemagglutinin (HA) genes and other segments. The severity of influenza was represented as the case fatality ratio (CFR) in the hospitalized cases. The CFRs in waves 1 and 2, the interwave period and wave 3 were 6.4%, 5.1%, 15.2% and 9.8%, respectively. The results highlight the association of virus evolution and variable influenza severity. Further analysis revealed that the major affected groups were shifted in the waves to older individuals, who had higher age-specific CFRs. The successive pandemic waves create challenges for the strategic preparedness of health authorities and make the pandemic uncertain and variable. Our findings indicate that the emergence of new variants and age shift to high fatality groups might contribute potentially to the occurrence of successive severe pandemic waves and offer insights into the adjustment of national responses to mitigate influenza pandemics.     

The Cost Effectiveness of Pandemic Influenza Interventions: A Pandemic Severity Based Analysis

Background

The impact of a newly emerged influenza pandemic will depend on its transmissibility and severity. Understanding how these pandemic features impact on the effectiveness and cost effectiveness of alternative intervention strategies is important for pandemic planning.

Methods

A cost effectiveness analysis of a comprehensive range of social distancing and antiviral drug strategies intended to mitigate a future pandemic was conducted using a simulation model of a community of ∼30,000 in Australia. Six pandemic severity categories were defined based on case fatality ratio (CFR), using data from the 2009/2010 pandemic to relate hospitalisation rates to CFR.

Results

Intervention strategies combining school closure with antiviral treatment and prophylaxis are the most cost effective strategies in terms of cost per life year saved (LYS) for all severity categories. The cost component in the cost per LYS ratio varies depending on pandemic severity: for a severe pandemic (CFR of 2.5%) the cost is ∼$9 k per LYS; for a low severity pandemic (CFR of 0.1%) this strategy costs ∼$58 k per LYS; for a pandemic with very low severity similar to the 2009 pandemic (CFR of 0.03%) the cost is ∼$155 per LYS. With high severity pandemics (CFR >0.75%) the most effective attack rate reduction strategies are also the most cost effective. During low severity pandemics costs are dominated by productivity losses due to illness and social distancing interventions, while for high severity pandemics costs are dominated by hospitalisation costs and productivity losses due to death.

Conclusions

The most cost effective strategies for mitigating an influenza pandemic involve combining sustained social distancing with the use of antiviral agents. For low severity pandemics the most cost effective strategies involve antiviral treatment, prophylaxis and short durations of school closure; while these are cost effective they are less effective than other strategies in reducing the infection rate.     

What about the ducks? An alternative vaccination strategy.

Like most emerging disease threats, avian influenza is a zoonotic disease maintained in nature by wildlife. In this case, the reservoir of infection is migratory waterfowl, primarily ducks. Rather than trying to vaccinate most of the world's human population in response to the threat of an avian influenza pandemic, it might be more prudent to vaccinate key reservoir wildlife species from which pandemic strains evolve. This strategy would require a much more intensive research effort to understand the evolution of avian influenza viruses in nature, but it would be far less costly than any of the alternatives. Research priorities for emerging zoonoses, such as new strains of avian influenza viruses, should be re-evaluated with an emphasis on ways to intervene at their source, the natural reservoir hosts from which they originate, rather than focusing up on human-based interventions, which are too often too late.     

Estimates of Japanese Encephalitis mortality and morbidity: A systematic review and modeling analysis

BackgroundJapanese Encephalitis (JE) is known for its high case fatality ratio (CFR) and long-term neurological sequelae. Over the years, efforts in JE treatment and control might change the JE fatality risk. However, previous estimates were from 10 years ago, using data from cases in the 10 years before this. Estimating JE disease severity is challenging because data come from countries with different JE surveillance systems, diagnostic methods, and study designs. Without precise and timely JE disease severity estimates, there is continued uncertainty about the JE disease burden and the effect of JE vaccination.MethodologyWe performed a systematic review to collate age-stratified JE fatality and morbidity data. We used a stepwise model selection with BIC as the selection criteria to identify JE CFR drivers. We used stacked regression, to predict country-specific JE CFR from 1961 to 2030. JE morbidity estimates were grouped from similar study designs to estimate the proportion of JE survivors with long-term neurological sequelae.Principal findingsWe included 82 and 50 peer-reviewed journal articles published as of March 06 2021 for JE fatality and morbidity with 22 articles in both analyses. Results suggested overall JE CFR estimates of 26% (95% CI 22, 30) in 1961–1979, 20% (95% CI 17, 24) in 1980–1999, 14% (95% CI 11, 17) in 2000–2018, and 14% (95% CI 11, 17) in 2019–2030. Holding other variables constant, we found that JE fatality risk decreased over time (OR: 0.965; 95% CI: 0.947–0.983). Younger JE cases had a slightly higher JE fatality risk (OR: 1.012; 95% CI: 1.003–1.021). The odds of JE fatality in countries with JE vaccination is 0.802 (90% CI: 0.653–0.994; 95% CI: 0.62–1.033) times lower than the odds in countries without JE vaccination. Ten percentage increase in the percentage of rural population to the total population was associated with 15.35% (95% CI: 7.71, 22.57) decrease in JE fatality odds. Ten percentage increase in population growth rate is associated with 3.71% (90% CI: 0.23, 7.18; 95% CI: -0.4, 8.15) increase in JE fatality odds. Adjusting for the effect of year, rural population percent, age of JE cases, and population growth rate, we estimated that there was a higher odds of JE fatality in India compared to China. (OR: 5.46, 95% CI: 3.61–8.31). Using the prediction model we found that, in 2000–2018, Brunei, Pakistan, and Timor-Leste were predicted to have the highest JE CFR of 20%. Bangladesh, Guam, Pakistan, Philippines, and Vietnam had projected JE CFR over 20% for after 2018, whereas the projected JE CFRs were below 10% in China, Indonesia, Cambodia, Myanmar, Malaysia, and Thailand. For disability, we estimated that 36% (min-max 0–85) JE patients recovered fully at hospital discharge. One year after hospital discharge, 46% (min-max 0%-97%) JE survivors were estimated to live normally but 49% (min-max 3% - 86%)till had neurological sequelae.ConclusionJE CFR estimates were lower than 20% after 2000. Our study provides an updated estimation of CFR and proportion of JE cases with long-term neurological sequelae that could help to refine cost-benefit assessment for JE control and elimination programs.     

Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: Assessing the effects of hypothetical interventions

Recurrent outbreaks of the avian H5N1 influenza virus in Asia represent a constant global pandemic threat. We characterize and evaluate hypothetical public health measures during the 1918 influenza pandemic in the Canton of Geneva, Switzerland. The transmission rate, the recovery rate, the diagnostic rate, the relative infectiousness of asymptomatic cases, and the proportion of clinical cases are estimated through least-squares fitting of the model to epidemic curve data of the cumulative number of hospital notifications. The latent period and the case fatality proportion are taken from published literature. We determine the variance and identifiability of model parameters via a simulation study. Our epidemic model agrees well with the observed epidemic data. We estimate the basic reproductive number for the spring wave R1;=1.49 (95% CI: 1.45-1.53) and the reproductive number for the fall wave R2;=3.75 (95% CI: 3.57-3.93). In addition, we estimate the clinical reporting for these two waves to be 59.7% (95% CI: 55.7-63.7) and 83% (95% CI: 79-87). We surmise that the lower reporting in the first wave can be explained by a lack of initial awareness of the epidemic and the relative higher severity of the symptoms experienced during the fall wave. We found that effective isolation measures in hospital clinics at best would only ensure control with probability 0.87 while reducing the transmission rate by >76.5% guarantees stopping an epidemic.     

What have we learnt from SARS?

With outbreaks of infectious disease emerging from animal sources, we have learnt to expect the unexpected. We were, and are, expecting a new influenza A pandemic, but no one predicted the emergence of an unknown coronavirus (CoV) as a deadly human pathogen. Thanks to the preparedness of the international network of influenza researchers and laboratories, the cause of severe acute respiratory syndrome (SARS) was rapidly identified, but there is no complacency over the global or local management of the epidemic in terms of public health logistics. The human population was lucky that only a small proportion of infected persons proved to be highly infectious to others, and that they did not become so before they felt ill. These were the features that helped to make the outbreak containable. The next outbreak of another kind of transmissible disease may well be quite different.     

Temporal estimates of case-fatality rate for COVID-19 outbreaks in Canada and the United States

BACKGROUND:Estimates of the case-fatality rate (CFR) associated with coronavirus disease 2019 (COVID-19) vary widely in different population settings. We sought to estimate and compare the COVID-19 CFR in Canada and the United States while adjusting for 2 potential biases in crude CFR.METHODS:We used the daily incidence of confirmed COVID-19 cases and deaths in Canada and the US from Jan. 31 to Apr. 22, 2020. We applied a statistical method to minimize bias in the crude CFR by accounting for the survival interval as the lag time between disease onset and death, while considering reporting rates of COVID-19 cases less than 50% (95% confidence interval 10%–50%).RESULTS:Using data for confirmed cases in Canada, we estimated the crude CFR to be 4.9% on Apr. 22, 2020, and the adjusted CFR to be 5.5% (credible interval [CrI] 4.9%–6.4%). After we accounted for various reporting rates less than 50%, the adjusted CFR was estimated at 1.6% (CrI 0.7%–3.1%). The US crude CFR was estimated to be 5.4% on Apr. 20, 2020, with an adjusted CFR of 6.1% (CrI 5.4%–6.9%). With reporting rates of less than 50%, the adjusted CFR for the US was 1.78 (CrI 0.8%–3.6%).INTERPRETATION:Our estimates suggest that, if the reporting rate is less than 50%, the adjusted CFR of COVID-19 in Canada is likely to be less than 2%. The CFR estimates for the US were higher than those for Canada, but the adjusted CFR still remained below 2%. Quantification of case reporting can provide a more accurate measure of the virulence and disease burden of severe acute respiratory syndrome coronavirus 2.

The risk of death associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is fundamental to the disease burden imposed by the coronavirus disease 2019 (COVID-19) pandemic. Quantification of this risk can provide critical information on the health and socioeconomic impact of the pandemic and identify population subgroups at highest risk for severe outcomes. The risk of death from a diagnosed infection, often referred to as the case-fatality rate (CFR), is the proportion of people who die from a disease among all those diagnosed with the disease over a certain period.Estimates of the COVID-19 CFR vary in different populations and at different stages of the outbreak, ranging from 0.4% in China¹ to 31.4% in the northwest region of Italy.² From individual-level data for patients in Hubei Province, Mainland China,³ an adjusted CFR of 3.6% (95% confidence interval [CI] 3.6%–3.8%) was estimated. For the outbreak on the Diamond Princess cruise ship, the age-adjusted CFR was estimated at 2.6% (95% CI 0.9%–6.7%) in all age groups but was substantially higher (13.0%, 95% CI 5.2%–26.0%) among those aged 70 years or older.⁴For ongoing outbreaks and especially during the exponential growth phase, the delay between onset of disease and knowledge of the final outcome may result in biased estimates of the CFR.⁵ Furthermore, underestimation of the number of COVID-19 cases will inflate the CFR. Limited ability to test or recognize mildly or moderately symptomatic people in both the United States and Canada has likely led to substantial underestimation of the rate of infection in affected communities.^6,7Given the importance of the CFR in public health planning, we sought to estimate the CFR for ongoing COVID-19 outbreaks in the US and Canada while accounting for preferential ascertainment of severe cases (leading to underestimation) and the lag time between disease onset and death.     

Optimal Control and Sensitivity Analysis of an Influenza Model with Treatment and Vaccination

We formulate and analyze the dynamics of an influenza pandemic model with vaccination and treatment using two preventive scenarios: increase and decrease in vaccine uptake. Due to the seasonality of the influenza pandemic, the dynamics is studied in a finite time interval. We focus primarily on controlling the disease with a possible minimal cost and side effects using control theory which is therefore applied via the Pontryagin’s maximum principle, and it is observed that full treatment effort should be given while increasing vaccination at the onset of the outbreak. Next, sensitivity analysis and simulations (using the fourth order Runge-Kutta scheme) are carried out in order to determine the relative importance of different factors responsible for disease transmission and prevalence. The most sensitive parameter of the various reproductive numbers apart from the death rate is the inflow rate, while the proportion of new recruits and the vaccine efficacy are the most sensitive parameters for the endemic equilibrium point.     

Detecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers

The outbreak of influenza A comes from a relatively stable state is a critical phenomenon on epidemic. In this paper, influenza A varying from different states is studied in the method of dynamical network biomarkers (DNB). Through studying DNB of influenza A virus protein, we can detect the warning signals of outbreak for influenza A and obtain a composite index. The composite index varies along with the state of pandemic influenza, which gives a clue showing the turn point of outbreak. The low value (<1) steady state of the composite index means influenza A is normally in the relatively steady stage. Meanwhile, if the composite index of a certain year increases by more than 0.8 relative to the previous year and it is less than 1 and it increases sharply and reaches a peak being larger than 1 in next year, it means the year is normal in the critical state before outbreak and the next year is normally in the outbreak state. Therefore, we can predict the outbreak of influenza A and identify the critical state before influenza A outbreak or outbreak state by observing the variation of index value.     

Retrospective investigation of an influenza A/H1N1pdm outbreak in an Italian military ship cruising in the Mediterranean Sea, May-September 2009

Background

Clinical surveillance may have underestimated the real extent of the spread of the new strain of influenza A/H1N1, which surfaced in April 2009 originating the first influenza pandemic of the 21^st century. Here we report a serological investigation on an influenza A/H1N1pdm outbreak in an Italian military ship while cruising in the Mediterranean Sea (May 24-September 6, 2009).

Methods

The contemporary presence of HAI and CF antibodies was used to retrospectively estimate the extent of influenza A/H1N1pdm spread across the crew members (median age: 29 years).

Findings

During the cruise, 2 crew members fulfilled the surveillance case definition for influenza, but only one was laboratory confirmed by influenza A/H1N1pdm-specific RT-PCR; 52 reported acute respiratory illness (ARI) episodes, and 183 reported no ARI episodes. Overall, among the 211 crew member for whom a valid serological result was available, 39.3% tested seropositive for influenza A/H1N1pdm. The proportion of seropositives was significantly associated with more crowded living quarters and tended to be higher in those aged <40 and in those reporting ARI or suspected/confirmed influenza A/H1N1pdm compared to the asymptomatic individuals. No association was found with previous seasonal influenza vaccination.

Conclusions

These findings underline the risk for rapid spread of novel strains of influenza A in confined environment, such as military ships, where crowding, rigorous working environment, physiologic stress occur. The high proportion of asymptomatic infections in this ship-borne outbreak supports the concept that serological surveillance in such semi-closed communities is essential to appreciate the real extent of influenza A/H1N1pdm spread and can constitute, since the early stage of a pandemic, an useful model to predict the public health impact of pandemic influenza and to establish proportionate and effective countermeasures.     

Impact of quarantine on the 2003 SARS outbreak: a retrospective modeling study

During the 2003 Severe Acute Respiratory Syndrome (SARS) outbreak, traditional intervention measures such as quarantine and border control were found to be useful in containing the outbreak. We used laboratory verified SARS case data and the detailed quarantine data in Taiwan, where over 150,000 people were quarantined during the 2003 outbreak, to formulate a mathematical model which incorporates Level A quarantine (of potentially exposed contacts of suspected SARS patients) and Level B quarantine (of travelers arriving at borders from SARS affected areas) implemented in Taiwan during the outbreak. We obtain the average case fatality ratio and the daily quarantine rate for the Taiwan outbreak. Model simulations is utilized to show that Level A quarantine prevented approximately 461 additional SARS cases and 62 additional deaths, while the effect of Level B quarantine was comparatively minor, yielding only around 5% reduction of cases and deaths. The combined impact of the two levels of quarantine had reduced the case number and deaths by almost a half. The results demonstrate how modeling can be useful in qualitative evaluation of the impact of traditional intervention measures for newly emerging infectious diseases outbreak when there is inadequate information on the characteristics and clinical features of the new disease-measures which could become particularly important with the looming threat of global flu pandemic possibly caused by a novel mutating flu strain, including that of avian variety.     

Pandemic influenza: overview of vaccines and antiviral drugs

Pandemic influenza has become a high priority item for all public health authorities. An influenza pandemic is believed to be imminent, and scientists agree that it will be a matter of when, where, and what will be the causative agent. Recently, most attention has been directed to human cases of avian influenza caused by a H5N1 avian influenza virus. An effective vaccine will be needed to substantially reduce the impact of an influenza pandemic. Current influenza vaccine manufacturing technology is not adequate to support vaccine production in the event of an avian influenza outbreak, and it has now become clear that new innovative production technology is required. Antiviral drugs, on the other hand, can play a very important role in slowing the disease spread but are in short supply and resistance has been a major issue. Here, we provide an update on the status of pandemic vaccine development and antiviral drugs. Finally, we conclude with some proposed areas of focus in pandemic vaccine preparedness.     

A sequential test for assessing the effectiveness of response strategies during an emerging epidemic

In epidemiology, the fatality rate is an important indicator of disease severity and has been used to evaluate the effects of new treatments. During an emerging epidemic with limited resources, monitoring the changes in fatality rate can also provide signals on the evaluation of government policies and healthcare quality, which helps to guide public health decision. A statistical test is developed in this paper to detect changes in fatality rate over time during the course of an emerging infectious disease. A major advantage of the proposed test is that it only requires the regularly reported numbers of deaths and recoveries, which meets the actual need as detailed surveillance data are hard to collect during the course of an emerging epidemic especially the deadly infectious diseases with large magnitude. In addition, with the sequential testing procedure, the effective measures can be detected at the earliest possible time to provide guidance to policymakers for swift action. Simulation studies showed that the proposed test performs well and is sensitive in picking up changes in the fatality rate. The test is applied to the 2014–2016 Ebola outbreak in Sierra Leone for illustration.     

Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation

Designing effective mitigation strategies against influenza outbreak requires an accurate prediction of a disease’s future course of spreading. Real time information such as syndromic surveillance data and influenza-like-illness (ILI) reports by clinicians can be used to generate estimates of the current state of spreading of a disease. Syndromic surveillance data are immediately available, in contrast to ILI reports that require data collection and processing. On the other hand, they are less credible than ILI data because they are essentially behavioral responses from a community. In this paper, we present a method to combine immediately-available-but-less-reliable syndromic surveillance data with reliable-but-time-delayed ILI data. This problem is formulated as a non-linear stochastic filtering problem, and solved by a particle filtering method. Our experimental results from hypothetical pandemic scenarios show that state estimation is improved by utilizing both sets of data compared to when using only one set. However, the amount of improvement depends on the relative credibility and length of delay in ILI data. An analysis for a linear, Gaussian case is presented to support the results observed in the experiments.     

Estimating the disease burden of 2009 pandemic influenza A(H1N1) from surveillance and household surveys in Greece

Background

The aim of this study was to assess the disease burden of the 2009 pandemic influenza A(H1N1) in Greece.

Methodology/Principal Findings

Data on influenza-like illness (ILI), collected through cross-sectional nationwide telephone surveys of 1,000 households in Greece repeated for 25 consecutive weeks, were combined with data from H1N1 virologic surveillance to estimate the incidence and the clinical attack rate (CAR) of influenza A(H1N1). Alternative definitions of ILI (cough or sore throat and fever>38°C [ILI-38] or fever 37.1–38°C [ILI-37]) were used to estimate the number of symptomatic infections. The infection attack rate (IAR) was approximated using estimates from published studies on the frequency of fever in infected individuals. Data on H1N1 morbidity and mortality were used to estimate ICU admission and case fatality (CFR) rates. The epidemic peaked on week 48/2009 with approximately 750–1,500 new cases/100,000 population per week, depending on ILI-38 or ILI-37 case definition, respectively. By week 6/2010, 7.1%–15.6% of the population in Greece was estimated to be symptomatically infected with H1N1. Children 5–19 years represented the most affected population group (CAR:27%–54%), whereas individuals older than 64 years were the least affected (CAR:0.6%–2.2%). The IAR (95% CI) of influenza A(H1N1) was estimated to be 19.7% (13.3%, 26.1%). Per 1,000 symptomatic cases, based on ILI-38 case definition, 416 attended health services, 108 visited hospital emergency departments and 15 were admitted to hospitals. ICU admission rate and CFR were 37 and 17.5 per 100,000 symptomatic cases or 13.4 and 6.3 per 100,000 infections, respectively.

Conclusions/Significance

Influenza A(H1N1) infected one fifth and caused symptomatic infection in up to 15% of the Greek population. Although individuals older than 65 years were the least affected age group in terms of attack rate, they had 55 and 185 times higher risk of ICU admission and CFR, respectively.     

Complete genome sequencing of a novel newcastle disease virus isolate circulating in layer chickens in the dominican republic

Newcastle disease virus (NDV) was isolated from an outbreak in layer chickens in the Dominican Republic in 2008. Infections with this isolate led to a 100% apparent case fatality rate in birds. Complete genome sequencing revealed that the isolate does not belong to any of the previously described NDV genotypes. Similarly, large differences were observed in the amino acid sequence of the fusion and hemagglutinin-neuraminidase proteins in comparison with all known NDV genotypes, suggesting the existence of an unknown reservoir for NDV. The work presented here represents the first complete genome sequence of NDV in the Dominican Republic.     

The beginning and ending of a respiratory viral pandemic-lessons from the Spanish flu

The COVID-19 pandemic goes into its third year and the world population is longing for an end to the pandemic. Computer simulations of the future development of the pandemic have wide error margins and predictions on the evolution of new viral variants of SARS-CoV-2 are uncertain. It is thus tempting to look into the development of historical viral respiratory pandemics for insight into the dynamic of pandemics. The Spanish flu pandemic of 1918 caused by the influenza virus H1N1 can here serve as a potential model case. Epidemiological observations on the shift of influenza mortality from very young and old subjects to high mortality in young adults delimitate the pandemic phase of the Spanish flu from 1918 to 1920. The identification and sequencing of the Spanish flu agent allowed following the H1N1 influenza virus after the acute pandemic phase. During the 1920s H1N1 influenza virus epidemics with substantial mortality were still observed. As late as 1951, H1N1 strains of high virulence evolved but remained geographically limited. Until 1957, the H1N1 virus evolved by accumulation of mutations (‘antigenic drift’) and some intratypic reassortment. H1N1 viruses were then replaced by the pandemic H2N2 influenza virus from 1957, which was in 1968 replaced by the pandemic H3N2 influenza virus; both viruses were descendants from the Spanish flu agent but showed the exchange of entire gene segments (‘antigenic shift’). In 1977, H1N1 reappeared from an unknown source but caused only mild disease. However, H1N1 achieved again circulation in the human population and is now together with the H3N2 influenza virus an agent of seasonal influenza winter epidemics.     

Measles Case Fatality Rate in Bihar,India, 2011–12

Background

Updated estimates of measles case fatality rates (CFR) are critical for monitoring progress towards measles elimination goals. India accounted for 36% of total measles deaths occurred globally in 2011. We conducted a retrospective cohort study to estimate measles CFR and identify the risk factors for measles death in Bihar–one of the north Indian states historically known for its low vaccination coverage.

Methods

We systematically selected 16 of the 31 laboratory-confirmed measles outbreaks occurring in Bihar during 1 October 2011 to 30 April 2012. All households of the villages/urban localities affected by these outbreaks were visited to identify measles cases and deaths. We calculated CFR and used multivariate analysis to identify risk factors for measles death.

Results

The survey found 3670 measles cases and 28 deaths (CFR: 0.78, 95% confidence interval: 0.47–1.30). CFR was higher among under-five children (1.22%) and children belonging to scheduled castes/tribes (SC/ST, 1.72%). On multivariate analysis, independent risk factors associated with measles death were age <5 years, SC/ST status and non-administration of vitamin A during illness. Outbreaks with longer interval between the occurrence of first case and notification of the outbreak also had a higher rate of deaths.

Conclusions

Measles CFR in Bihar was low. To further reduce case fatality, health authorities need to ensure that SC/ST are targeted by the immunization programme and that outbreak investigations target for vitamin A treatment of cases in high risk groups such as SC/ST and young children and ensure regular visits by health-workers in affected villages to administer vitamin A to new cases.     

Pandemic management and developing world bioethics: bird flu in West Bengal

This paper examines the case of a recent H5N1virus (avian influenza) outbreak in West Bengal, an eastern state of India, and argues that poorly executed pandemic management may be viewed as a moral lapse. It further argues that pandemic management initiatives are intimately related to the concept of health as a social 'good' and to the moral responsibility of protection from foreseeable social harm from an infectious disease. The initiatives, therefore, have to be guided by special moral obligations towards biorisk reduction, obligations which remain unfulfilled when a public body entrusted with the responsibility fails to manage satisfactorily the prevention and control of the infection. The overall conclusion is that pandemic management has a moral dimension. The gravity of the threat that fatal infectious diseases pose for public health creates special moral obligations for public bodies in pandemic situations. However, the paper views the West Bengal case as a learning opportunity, and considers the lapses cited as challenges that better, more effectively conducted pandemic management can prepare for. It is hoped that this paper will provoke constructive bioethical deliberations, particularly pertinent to the developing world, on how to ensure that the obligations towards health are fulfilled ethically and more effectively.