Spatio-temporal patterns of proportions of influenza B cases

A potential Chinese medicine monomer against influenza A virus and influenza B virus: isoquercitrin

BACKGROUND

Influenza viruses, especially Influenza A virus and Influenza B virus, are respiratory pathogens and can cause seasonal epidemics and pandemics. Severe influenza viruses infection induces strong host-defense response and excessive inflammatory response, resulting in acute lung damage, multiple organ failure and high mortality. Isoquercitrin is a Chinese medicine monomer, which was reported to have multiple biological activities, including antiviral activity against HSV, IAV, SARS-CoV-2 and so on. Aims of this study were to assess the in vitro anti-IAV and anti-IBV activity, evaluate the in vivo protective efficacy against lethal infection of the influenza virus and searched for the more optimal method of drug administration of isoquercitrin.

METHODS

In vitro infection model (MDCK and A549 cells) and mouse lethal infection model of Influenza A virus and Influenza B virus were used to evaluate the antiviral activity of isoquercitrin.

RESULTS

Isoquercitrin could significantly suppress the replication in vitro and in vivo and reduced the mortality of mouse lethal infection models. Compared with virus infection group, isoquercitrin mitigated lung and multiple organ damage. Moreover, isoquercitrin blocked hyperproduction of cytokines induced by virus infection via inactivating NF-κB signaling. Among these routes of isoquercitrin administration, intramuscular injection is a better drug delivery method.

CONCLUSION

Isoquercitrin is a potential Chinese medicine monomer Against Influenza A Virus and Influenza B Virus infection.

Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic

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.

Strategic decision making about travel during disease outbreaks: a game theoretical approach

Visitors can play an important role in the spread of infections. Here, we incorporate an epidemic model into a game theoretical framework to investigate the effects of travel strategies on infection control. Potential visitors must decide whether to travel to a destination that is at risk of infectious disease outbreaks. We compare the individually optimal (Nash equilibrium) strategy to the group optimal strategy that maximizes the overall population utility. Economic epidemiological models often find that individual and group optimal strategies are very different. By contrast, we find perfect agreement between individual and group optimal strategies across a wide parameter regime. For more limited regimes where disagreement does occur, the disagreement is (i) generally very extreme; (ii) highly sensitive to small changes in infection transmissibility and visitor costs/benefits; and (iii) can manifest either in a higher travel volume for individual optimal than group optimal strategies, or vice versa. The simulations show qualitative agreement with the 2003 severe acute respiratory syndrome (SARS) outbreak in Beijing, China. We conclude that a conflict between individual and group optimal visitor travel strategies during outbreaks may not generally be a problem, although extreme differences could emerge suddenly under certain changes in economic and epidemiological conditions.

Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 - a global mortality impact modeling study

BACKGROUND

A global pandemic mortality study found prominent regional mortality variations in 2009 for Influenza A(H1N1)pdm09. Our study attempts to identify factors that explain why the pandemic mortality burden was high in some countries and low in others.

METHODS

As a starting point, we identified possible risk factors worth investigating for Influenza A(H1N1)pdm09 mortality through a targeted literature search. We then used a modeling procedure (data simulations and regression models) to identify factors that could explain differences in respiratory mortality due to Influenza A(H1N1)pdm09. We ran sixteen models to produce robust results and draw conclusions. In order to assess the role of each factor in explaining differences in excess pandemic mortality, we calculated the reduction in between country variance, which can be viewed as an effect-size for each factor.

RESULTS

The literature search identified 124 publications and 48 possible risk factors, of which we were able to identify 27 factors with appropriate global datasets. The modelling procedure indicated that age structure (explaining 40% of the mean between country variance), latitude (8%), influenza A and B viruses circulating during the pandemic (3-8%), influenza A and B viruses circulating during the preceding influenza season (2-6%), air pollution (pm10; 4%) and the prevalence of other infections (HIV and TB) (4-6%) were factors that explained differences in mortality around the world. Healthcare expenditure, levels of obesity, the distribution of antivirals, and air travel did not explain global pandemic mortality differences.

CONCLUSIONS

Our study found that countries with a large proportion of young persons had higher pandemic mortality rates in 2009. The co-circulation of influenza viruses during the pandemic and the circulation of influenza viruses during the preceding season were also associated with pandemic mortality rates. We found that real time assessments of 2009 pandemic mortality risk factors (e.g. obesity) probably led to a number of false positive findings.