Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic

Abstract

Background: Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.Methods: Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (P A ) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW A ) and influenza B (RW B ). Third, the average positive rates of influenza A (R A ) and influenza B (R B ) for each country were calculated by averaging RW A , and RW B of 52 weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in P A in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate R A and R B in all countries. Results: P A ranged from 0.43 in Zambia to 0.98 in Belarus, and P A in countries with higher income was greater than those countries with lower income. The spatial patterns of high R B were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RW A peaked in early weeks in temperate countries, and the peak of RW B occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).Conclusions: Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.

Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

“…From Fig. 2, it could be seen there was a slight peak in winter and spring (From the 47 th to 52 nd week, 14 and from st to 9 th week), similar to the result of the whole country [26].…”

“…Some previous researches engaged in constructing the influenza transmission network by temporal and spatial statistics [11][12][13][14][15][16]. For example, Alonso WJ [16] used Fourier decomposition to find a seasonal southward traveling wave of influenza across Brazil originating from equatorial and low population regions in March-April and moving towards temperate and highly popular regions over a 3-month period.…”

Coronavirus Seasonality, Respiratory Infections and Weather

“…In uenza B and human metapneumovirus peak a few weeks earlier around the New Year, with infection extending into the spring. Parain uenza 1 and 2 viruses peak in the autumn/winter period between weeks 40 and 50, parain uenza 3 has a spring peak (weeks [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], human metapneumovirus and in uenza B virus increase at the end of the year, with infection extending into the spring. Enteric viruses (coxsackievirus A and B; echovirus; enterovirus) have summer peaks (weeks [25][26][27][28][29][30][31][32][33], while adenovirus and rhinoviruses are relatively unseasonal and echovirus, coxsackie A and coxsackie B are more sporadic in nature.…”

Weather Variability and COVID-19 Transmission: A Review of Recent Research

“…The effects of weather variability on COVID-19 transmission is an emerging area of interest; as COVID-19 has similar transmission modes to other respiratory viruses such as seasonal influenza, it is predicted that SARS-COV-2 could have a similar relationship with weather variables such as temperature, humidity, rainfall and wind speed [ 11 ]. Weather and infectious diseases are linked, with the potential for weather variability to favor the emergence of novel viruses and contribute to disease transmission, morbidity and mortality, understanding global spatial and temporal patterns of COVID-19 transmission is vital in the control and prevention of future outbreaks [ 12 ]. Due to the widespread and continuing transmission of COVID-19, it is predicted that COVID-19 outbreaks will persist into the future and potentially exhibit a seasonal outbreak profile similar to influenza and other infectious respiratory diseases [ 13 , 14 ].…”