Adaptive vaccination strategies to mitigate pandemic influenza: Mexico as a case study

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

Impacts of a mass vaccination campaign against pandemic H1N1 2009 influenza in Taiwan: a time-series regression analysis

OBJECTIVES

A multicenter, hospital-wide, clinical and epidemiological study was conducted to assess the effectiveness of the mass influenza vaccination program during the 2009 H1N1 influenza pandemic, and the impact of the prioritization strategy among people at different levels of risk.

METHODS AND RESULTS

Among the 34 359 medically attended patients who displayed an influenza-like illness and had a rapid influenza diagnostic test (RIDT) at one of the three participating hospitals, 21.0% tested positive for influenza A. The highest daily number of RIDT-positive cases in each hospital ranged from 33 to 56. A well-fitted multiple linear regression time-series model (R(2)=0.89) showed that the establishment of special community flu clinics averted an average of nine cases daily (p=0.005), and an increment of 10% in daily mean level of population immunity against pH1N1 through vaccination prevented five cases daily (p<0.001). Moreover, the regression model predicted five-fold or more RIDT-positive cases if the mass influenza vaccination program had not been implemented, and 39.1% more RIDT-positive cases if older adults had been prioritized for vaccination above school-aged children.

CONCLUSIONS

Mass influenza vaccination was an effective control measure, and school-aged children should be assigned a higher priority for vaccination than older adults during an influenza pandemic.

Gradual changes in the age distribution of excess deaths in the years following the 1918 influenza pandemic in Copenhagen: using epidemiological evidence to detect antigenic drift

BACKGROUND

The 1918 influenza pandemic was associated with an unusual age pattern of mortality, with most deaths occurring among young adults. Few studies have addressed changes in the age distribution for influenza-related mortality in the pre-pandemic and post-pandemic period, which has implications for pandemic preparedness. In the present paper, we analyse the age patterns of influenza-related excess mortality in the decades before and after the 1918 pandemic, using detailed historic surveillance data from Copenhagen.

METHODS

Weekly age-specific rates of respiratory mortality and influenza-like-illnesses were compiled for 1904-1937. Seasonal excess rates of morbidity and mortality attributable to influenza were calculated using a seasonal regression approach. To characterize the age patterns of influenza-related deaths in individual seasons, we used two rate ratio (RR) measures representing ratios of excess mortality rates between age groups and influenza seasons.

RESULTS

Individuals aged 15-64 years experienced sharply elevated excess respiratory mortality rates in the 1918-1919 and 1919-1920 pandemic periods, compared to pre-pandemic seasons (RR for excess mortality in the fall of 1918 = 67 relative to inter-pandemic seasons). Of all excess respiratory deaths occurring during 1918-1919, 84% were reported in individuals 15-64 years. By contrast, seniors over 65 years of age experienced no measurable excess mortality during 1918-1919 and moderate excess mortality in the recrudescent pandemic wave of 1919-1920. The first post-pandemic season associated with high excess mortality rates in individuals over 65 years was 1928-1929, with 73% of excess deaths occurring among seniors. We estimate that the age patterns of influenza-related mortality returned to pre-pandemic levels after 1925, based on trends in the rate ratio of excess respiratory mortality in people under and over 65 years.

CONCLUSIONS

The unusual elevation of excess respiratory mortality rates in young and middle-aged adults was confined to the first three years of A/H1N1 virus circulation 1918-1920; the rapid return to "epidemic" mortality pattern in this age group was probably due to high attack rates and build-up of immunity. In contrast, seniors were completely spared from pandemic mortality during 1918-1919, likely due to childhood exposure to an A/H1-like influenza virus. The rise in excess mortality rates in seniors in the recrudescent pandemic wave of 1919-1920 may suggest the emergence of an early influenza A/H1N1 drift variant. Subsequent drift events may have been associated with the particularly severe 1928-1929 epidemic in Denmark and elsewhere.

Modeling vaccination campaigns and the Fall/Winter 2009 activity of the new A(H1N1) influenza in the Northern Hemisphere

The unfolding of pandemic influenza A(H1N1) for Fall 2009 in the Northern Hemisphere is still uncertain. Plans for vaccination campaigns and vaccine trials are underway, with the first batches expected to be available early October. Several studies point to the possibility of an anticipated pandemic peak that could undermine the effectiveness of vaccination strategies. Here, we use a structured global epidemic and mobility metapopulation model to assess the effectiveness of massive vaccination campaigns for the Fall/Winter 2009. Mitigation effects are explored depending on the interplay between the predicted pandemic evolution and the expected delivery of vaccines. The model is calibrated using recent estimates on the transmissibility of the new A(H1N1) influenza. Results show that if additional intervention strategies were not used to delay the time of pandemic peak, vaccination may not be able to considerably reduce the cumulative number of cases, even when the mass vaccination campaign is started as early as mid-October. Prioritized vaccination would be crucial in slowing down the pandemic evolution and reducing its burden.