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To support the ongoing management of viral respiratory diseases, many countries are moving towards an integrated model of surveillance for SARS-CoV-2, influenza, and other respiratory pathogens. While many surveillance approaches catalysed by the COVID-19 pandemic provide novel epidemiological insight, continuing them as implemented during the pandemic is unlikely to be feasible for non-emergency surveillance, and many have already been scaled back. Furthermore, given anticipated co-circulation of SARS-CoV-2 and influenza, surveillance activities in place prior to the pandemic require review and adjustment to ensure their ongoing value for public health. In this perspective, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies, their contribution to epidemiological assessment, forecasting, and public health decision making.

Additional file 1. IHME Inpatient Data Metadata. Description of data: Detailed information including number of years of data, length of ICD codes, and total number of inpatient admissions for each source of clinical administrative data used in this study. All data is in the custody of the Institute of Health Metrics and Evaluation, and is available in the Global Health Data Exchange (ghdx.healthdata.org).

Additional file 2. Influenza and RSV ICD Codes. Description of data: The ICD-9 and ICD-10 codes used to identify influenza and RSV admissions from raw ICD extraction, to compare against the utilization rates produced by the BIRD study.

Additional file 1: Table S1. COVID-19 vaccine priority groups. Figure S1. Schematic diagram of vaccinating 70% of target populations by Tiers. Table S2. Characteristics of systematic reviews for persons at high risk of severe outcome of COVID-19. Table S3. Age distribution of COVID-19 cases. Table S4. Data used to estimate the COVID-19 incidence rate. Figure S2. Pooled incidence of COVID-19 cases, stratified by age. Figure S3. Sensitivity analyses on days needed to vaccinate 90% of the target population, under the assumption that 10 million doses are administered per day. Figure S4. Sensitivity analyses on days needed to vaccinate 70% of the target population, under the assumption that 3 million doses are administered per day. Figure S5. Sensitivity analyses on days needed to vaccinate 90% of the target population, under the assumption that 3 million doses are administered per day. Figure S6. Sensitivity analyses on days needed to vaccinate 70% of the target population, under the assumption that 20 million doses are administered per day. Figure S7. Sensitivity analyses on days needed to vaccinate 90% of the target population, under the assumption that 20 million doses are administered per day.

Figure S1. Trend in the number of AMR models per year. Figure S2. (a) Field-weighted citation impact (FWCI) of AMR publications by year (excludes 3 publications with FWCI greater than 10) and (b) FWCI by pathogen category: viral (V), bacterial (B), parasitic (P), and fungal (F). Figure S3. Pathogen trends over time. The proportion of articles on selected infectious diseases relative to the total publications per year published between 2006 and 2016 (articles/year): healthcare-acquired infections (HCAI), methicillin-resistant staphylococcus aureus (MRSA), tuberculosis (TB), human immunodeficiency virus (HIV), influenza and plasmodium falciparum (malaria). Figure S4. Model characteristic trends over time: The proportion of articles with a particular model approach (deterministic or stochastic, compartmental or individual-based models) or rigor (calibrated or validated) by the total publications per year published between 2006 and 2016. Table S1. References for publications divided by diseases they model. Table S2. References for publications divided by model class. Table S3. References for publications divided by disease specific interventions. (DOCX 2019â kb)

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist. (DOC 62â kb)

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: “cold-dry” and “humid-rainy”. For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11–12 g/kg and 18–21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates.