Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors

Abstract

Influenza constitutes a major challenge to world health authorities due to high transmissibility and the capacity to generate large epidemics. This study aimed to characterize the diffusion process of influenza A (H1N1) by identifying the starting point of the epidemic as well as climatic and sociodemographic factors associated with the occurrence and intensity of transmission of the disease. The study was carried out in the Brazilian state of Paraná, where H1N1 caused the largest impact. The units of spatial and temporal analysis were the municipality of residence of the cases and the epidemiological weeks of the year 2009, respectively. Under the Bayesian paradigm, parametric inference was performed through a two-part spatiotemporal model and the integrated nested Laplace approximation (INLA) algorithm. We identified the most likely starting points through the effective distance measure based on mobility networks. The proposed estimation methodology allowed for rapid and efficient implementation of the spatiotemporal model, and provided evidence of different patterns for chance of occurrence and risk of influenza throughout the epidemiological weeks. The results indicate the capital city of Curitiba as the probable starting point, and showed that the interventions that focus on municipalities with greater migration and density of people, especially those with higher Human Development Indexes (HDIs) and the presence of municipal air and road transport, could play an important role in mitigation of effects of future influenza pandemics on public health. These results provide important information on the process of introduction and spread of influenza, and could contribute to the identification of priority areas for surveillance as well as establishment of strategic measures for disease prevention and control. The proposed model also allows identification of epidemiological weeks with high chance of influenza occurrence, which can be used as a reference criterion for creating an immunization campaign schedule.

Roles for Pathogen Interference in Influenza Vaccination, with Implications to Vaccine Effectiveness (VE) and Attribution of Influenza Deaths

“…It is widely recognized that infectious outbreaks show extreme spatiotemporal variation (space–time variation) both between countries and within a country [ 86 , 87 , 88 , 89 , 90 , 91 ]. As an example, the winter of 2014/15 saw high international excess winter mortality, partly due to the emergence of new influenza A/H1N1 clade(s) [ 3 ].…”

Geographic components of SARS-CoV-2 expansion: a hypothesis

“…Similar to other zoonotic viruses (e.g., H1N1 influenza) ( 23 , 52 ), factors such as temperature and humidity can influence SARS-CoV-2 infectivity ( 89 ) although the links appear to be quite dynamic ( 15 ). Lower temperatures and hypobaric-hypoxia at HA (above ~2,000 m) could, in part, explain the lower incidence of infections at HA, as they render the environment uninhabitable to non-human living vectors (e.g., Aedes aegypti mosquitos, flies, or other pests) ( 12 , 53 ); however, recent evidence indicates that Aedes mosquitos do not pose a threat to SARS-CoV-2 transmission ( 86 ).…”

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China

“…For instance, the Ensemble Empirical Mode Decomposition (EEMD) analysis indicated the limited seasonal modulations on COVID-19 evolution ( Huang et al, 2021 ). It has been also reported that high altitudes can influence the occurrence and intensity of influenza A (H1N1, H5N1, H5N8) ( da Costa et al, 2018 ; Scolamacchia et al, 2021 ), and decrease COVID-19 infection ( Segovia-Juarez et al, 2020 ; Stephens et al, 2021 ). The high altitude at 4500 m down-regulates the expression of ACE2, thereby probably protecting them against COVID-19 replication in host cells ( Mendes et al, 2019 ).…”