Flu Season 2019-2020

Influenza viruses are negative-sense single stranded RNA viruses of the Orthomyxoviridae family. There are four types of influenza viruses: A, B, C and D . In electron microscopy these types, especially A and B,  could be virtually indistinguishable. D is more similar to C.

Most of these viruses look like small spheres with spikes, although some could be irregularly shaped. Spikes are proteins and they are what's different in different forms of influenza. Typically, influenza A starts the early wave of the flu, and influenza B starts to show up at the tail-end of the season, in early spring. This year is't flu B that started the first wave while flu A may be responsible for the second wave. Is the flu shot working? It's still too early to predict.

Will you get the flu this season?

Worst of flu season may be over. But you can still catch a chill. If you shake hands with lots of sick people, for example. Or don't keep sufficiently warm. Yes, your mother has told you, and you thought it was just an old wives' tale, but it wasn't. Scientists (Johnson and Eccles, 2005) provide evidence that cold exposure may induce cold symptoms without any contact to sick individuals. As we all carry dormant (sub-clinical) infections in our nose, genitals and other parts of the body, and these viruses may get reactivated. Ever noticed the need to blow nose after spending some time in cold air? Your body might be trying to expel the waking-up microbes.

Emerging health analysis software tools like Aurametrix aim at keeping us healthy by warning about symptoms and diseases. Prolonged exposure to cold means that 4-5 days after the exposure there is 10% probability of developing nasal stuffiness, sneezing, throat irritation of mild fever. 10% if Aurametrix knows nothing else about you. Higher if you are in the most vulnerable age & health conditions group, have a history of more frequent cold infections in prior years or were recently exposed to other stressors. Aurametrix can draw additional conclusions from looking at ingredients in your diet and chemicals in your environment.  Medical records could add another thousand variables. Medical codes given to every documented complaint, prior medications, procedures, information about attending doctors and payments were shown to help predict C.difficile infections in hospitals using machine learning (Wiens, Guttag, Horvitz, 2012).

Social media (in addition to notifications by official sources) keeps us more aware and more afraid of the flu. But what if we are not able to keep away from exposure to a virus, forgot to clean our hands and could not avoid a non-ventilated area with sneezing sick people? The good news is that if we did everything else right we have a fighting chance. As it was shown in a scientific study (Huang et al., 2011), only 9 out of 17 healthy human volunteers exposed to H3N2 virus developed mild to severe symptoms.

So be happy to be healthy, in addition to doing your best to stay flu-free.

Image Credits: Allison Morris, OnlineEducation.net  

REFERENCES

Johnson C, & Eccles R (2005). Acute cooling of the feet and the onset of common cold symptoms. Family practice, 22 (6), 608-13 PMID: 16286463

Jankowski R, Philip G, Togias A, Naclerio R. Demonstration of bilateral cholinergic secretory response after unilateral nasal cold, dry air challenge. Rhinology 1993; 31: 97-100

J. Wiens, J. Guttag, E. Horvitz. Learning Evolving Patient Risk Processes for C. Diff Colonization, Machine Learning for Clinical Data Analysis, ICML 2012, Edinburgh, Scotland, June 2012.

Huang Y, Zaas AK, Rao A, Dobigeon N, Woolf PJ, Veldman T, Øien NC, McClain MT, Varkey JB, Nicholson B, Carin L, Kingsmore S, Woods CW, Ginsburg GS, & Hero AO 3rd (2011). Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection. PLoS genetics, 7 (8) PMID: 21901105

More apps, less flu?

Fewer people caught the flu this season compared with  past years. And many more apps tracking the flu have been developed.  Any relationship between these two trends?

Of course, less flu could be just the result of fewer mutations in bugs, warmer weather and more vaccinations. Yet the power of good software - such as google flu trends, twitter-based trackers and numerous apps can not be underestimated. Thanks to these tools, we are now more aware (and more afraid).

The flu is inherently social. "Nip the flu in the bud by spreading information, not germs, through the social network", says Flu Alert app. and lets you sort your friends by their flu exposures. Virtual flu in Fluville is promoting healthy habits by showing how flu can spread. Fluspotter let's you exchange warnings with your facebook friends, Flutracking reads your e-mails, while Influ takes your voice messages and shares it with users around the world. Biodisapora is tracking disease outbreaks by monitoring air travel. Sickweather scans Twitter and Facebook posts, and Germtrax lets you also sync with Foursquare and Google+  to geo-locate your wereabouts while being sick - with one of 6067 sicknesses available in their database.

According to multiple research studies, flu-related Internet searches, use of certain phrases on Twitter and Facebook posts peak 1-2 weeks earlier than the epidemic curve and align reasonably well with CDC data.

Social media is a noisy but powerful adjunct to surveillance systems based on official sources. It gives us an opportunity of contributing to the community's common good. It raises our awareness, but is not sufficient on its own. Many other factors increase our individual risks. Air travel. Or stress (haven't you noticed flu season in Greece was the worst in the world this year?) Age and food, too.

Aurametrix is a personal health analysis system that tackles this problem with an integrative approach. It aligns your medical history and historical CDC information with your food, mood and amount of sleep. It  looks at all environmental predictions for today telling you if pollen, mold spores or air quality are more likely to be the reason for your symptoms, or if it is the rise in infectious diseases in your area. Aurametrix relies on a range of official sources and social media predictions. The data are constantly updated and refined, and causes linked with effects.

REFERENCES

Dugas, A., Hsieh, Y., Levin, S., Pines, J., Mareiniss, D., Mohareb, A., Gaydos, C., Perl, T., & Rothman, R. (2012). Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics Clinical Infectious Diseases, 54 (4), 463-469 DOI: 10.1093/cid/cir883

Manago, Adriana M., Taylor, T., Greenfield, P.M. Me and my 400 friends: The anatomy of college students' Facebook networks, their communication patterns, and well-being. (2012) Developmental Psychology, Jan 30. doi: 10.1037/a0026338

Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One. 2011 May 4;6(5):e19467. PMID: 21573238

Ginsberg J, et al. Detecting influenza epidemics using search engine query data. (2009) Nature 457, 1012–1014.

Ortiz JR, et al. Monitoring influenza activity in the United States: A comparison of traditional surveillance systems with Google Flu Trends. PLoS ONE 6(4):e18687. 2011.

Christakis NA, et al. Social network sensors for early detection of contagious outbreaks. PLoS ONE 5(9):e12948. 2010.

Malik MT, Gumel A, Thompson LH, Strome T, Mahmud SM. "Google flu trends" and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health. 2011 Jul-Aug;102(4):294-7. PMID: 21913587

Collier N, Son NT, Nguyen NM. OMG U got flu? Analysis of shared health messages for bio-surveillance. J Biomed Semantics. 2011 Oct 6;2 Suppl 5:S9. PMID: 22166368

Basak P. Development of an online tool for public health: the European Public Health Law Network.
Public Health. 2011 Sep;125(9):600-3. Epub 2011 Aug 23. PMID: 21864871

Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One. 2010 Nov 29;5(11):e14118. PMID: 21124761

Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: twitter and antibiotics. Am J Infect Control. 2010 Apr;38(3):182-8. PMID: 20347636

Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009 Mar 27;11(1):e11. PMID: 19329408

Flu update: May through August

  
In May, June, July and August 2011, Influenza activity was low in North America, Europe, Northern Asia, Middle East and North Africa. Tepid latitudes of Northern hemisphere remained at baseline inter-seasonal levels.

The 2011 influenza season in South Africa peaked in the end of June. The majority of samples were identified as 2009 H1N1 virus. Flu levels were still moderate in early July but illness activity became low by the end of July.

Dominican Republic, Cuba, Honduras and Brazil observed moderate activity (peak was in the end of June/beginning of July). Columbia had active circulation of 2009 H1N1 virus, but there were no new cases in July and August. Illness levels in Peru and Bolivia also returned to low levels.

Flu activity in August was low in countries of Western Africa (Ghana, Cameroon and Senegal), and Southern Asia (India, Bangladesh, Thailand, and Singapore).  Google flu trends predicted increased activity for South Africa and Chile in June-July and Uruguay in July-August. WHO data showed that flu levels returned to low in South Africa, but started to increase for Chile, Uruguay and Argentina marking the peak of the season.

In Australia, influenza-like illness consultations and laboratory-confirmed cases continue to increase.
The flu season started in the end of April, rates slightly declined in mid May but kept increasing since.  The most common virus was influenza A(H1N1)2009 but influenza B was also prevalent, unevenly distributed across the country.

According to the ESR Kenepuru Science Centre and the WHO, flu incidents in New Zealand increased from mid-May through the end of July, but decreased during the first two weeks of August. ILI activity in New Zealand remained around expected levels and the majority of viruses detected have been influenza B.

See the latest CDC  Global and WHO updates for more.

Flu activity increasing in Europe, remains moderate but widespreads in US

In the first week of January, United Kingdom and Denmark had the highest flu circulation in Europe, (High in England and Ireland, moderate in Whales and Scotland), followed by Belgium, France, Israel, Italy, Luxemburg, Spain, and Malta.  Ukraine and Russia also experienced high flu activity. Most flu isolates are 2009 H1N1.
In Japan, there were more flu incidents, mostly of 2009 H1N1 origin. Google flu map predicted lower activity for Japan and highest for Norway, lowest for the Southern hemisphere. 

In the United States, influenza-like illness (ILI) was down the week before but still above the national baseline. Yet three more states reported widespread flu than the week before, updating the list to: Alabama, Arizona, Georgia, Kentucky, Louisiana, New York, North Carolina, and Virginia. New York City and six states: Alabama, Georgia, Illinois, Louisiana, Mississippi, and Oklahoma reported high ILI activity, Nevada and New Jersey reported moderate ILI activity, and the remaining states reported either low or minimal activity. No antibiotic resistant strains have been found.

Influenza surveillance: US update

Flu can lead to despair, cause various complications and exacerbate chronic health conditions.Knowledge is the primary weapon in preventing and defeating it.

Influenza-like illness (ILI) in the United States was still below national baseline in mid December 2010, but kept increasing throughout the month. Three states of region 4, Alabama, Georgia, and Mississippi, experienced high ILI activity, New York City, Nevada and Arizona experienced moderate ILI activity, Oklahoma and Louisiana experienced low ILI activity, and the District of Columbia and remaining 43 states experienced minimal ILI activity. The geographic spread of influenza in one state (Mississippi) was reported as widespread, Puerto Rico and 13 states reported regional activity, seven states reported local activity; the District of Columbia, Guam, and 28 states reported sporadic activity. CDC data  are based on surveillance for week 50 (December 12-18, 2010) and 3 prior weeks. Google flu trends for week 51 (December 19- 25, top figure) predict increase for Utah (region 8) and Kentucky  along with other states of region 4.


What to expect next? Hopefully a good year like  2005-2006, shown on the left. Or a slightly worse scenario with more widespread flu around the month of February,  like in 2007-2008 (as seen on the right).