Ebola Update: Death in Nigeria, Impact on Healthcare Workers, and Containment Measures in Liberia

Jul 29, 2014 | Alexandra Thomsen | Outbreak News

Nations around the world have voiced concern about West Africa’s Ebola outbreak spreading via air travel, with warnings like “Ebola only a plane ride away.” In April, Nigerian Minister of Health Onyebuchi Chukwu admitted that “Nigeria is in danger,” but assured that the government would emphasize education about the disease and implement preventive measures. Unfortunately, on July 25, the first Ebola case was reported in Nigeria. The case was a Liberian man who collapsed at Nigeria’s main airport in Lagos upon arrival from Monrovia, Liberia, and succumbed to the disease soon after. Health Minister Chukwu announced that tests from the Lagos University Teaching Hospital confirmed the diagnosis, although both the WHO and Lagos state government claimed that they were still awaiting lab confirmation as of July 29. Now, the Lagos hospital to which the patient was taken has been quarantined and health officials are monitoring 59 contacts. The WHO is also sending teams to conduct follow-up work in Nigeria and Togo, where the plane made a brief layover.

To date, Ebola has infected at least 1201 people in West Africa and 672 have died. The disease has taken a major toll on healthcare workers—at least 100 have been infected and about 50% have died, according to the WHO. One of Liberia’s most high-profile doctors, Dr. Samuel Brisbane, died of the disease on Saturday. A Ugandan doctor working in Liberia died earlier this month and two Americans have also fallen ill. One, Dr. Kent Brantly, was working with Samaritan’s Purse, a North Carolina-based medical charity, when he began showing signs of Ebola. The other, Nancy Writebol, was working as a missionary with a joint SIM/Samaritan’s Purse team in Monrovia.

On July 28, Liberia implemented several stringent health measures in an attempt to slow the spread of the disease. President Ellen Johnson Sirleaf announced nationwide border closures except at major entry points, including the Roberts International Airport, James Spriggs Payne Airport, Foya Crossing, Bo Waterside Crossing, and Ganta Crossing. She said that preventive measures and testing centers would be established at these entry points. Due to this state of emergency, public gatherings—such as marches and demonstrations—will be restricted. In addition, Liberian government facilities and public places will be required to provide access to hand washing and other sanitization services. Hotels, restaurants, and entertainment centers will be mandated to play short informational clips on Ebola awareness and prevention.

Mistrust of doctors and concerns about stigmatization may mean that the real danger is an “epidemic of fear,” as Tony Barnett, a professor at the London School of Hygiene and Tropical Medicine, put it. Hostile mobs have confronted healthcare workers and physical barriers have blocked them from entering villages. Doctors Without Borders has classified twelve villages in Guinea as “red,” meaning that they may contain Ebola but are too unsafe to travel to. Local residents fear the emergency treatment center set up in Gueckedou because when patients enter, “they don’t leave alive.” These fears may stem from a lack of education about the disease that leads to misunderstanding about its transmission and treatment as well as mistrust of the outsiders who come to help. Some believe that Ebola is a curse that can only be healed spiritually, while others believe the disease is just a ruse. One former nurse in Kenema, Sierra Leone spread a rumor that Ebola was invented to conceal “cannibalistic rituals” being performed at the hospital. Because of the widespread fears of Ebola and medical treatment, providing education about the disease will be critical to bringing this outbreak under control. 

For a map on Ebola outbreak news, including articles and up-to-date case counts, visit healthmap.org/ebola.

World Hepatitis Day 2014

Jul 28, 2014 | Alexandra Thomsen | Research & Policy

Monday, July 28 marks World Hepatitis Day, a global public health campaign led by the WHO to raise awareness about the five hepatitis viruses (A, B, C, D, and E). Through this campaign, the WHO urges people worldwide to “think again” about hepatitis, an often neglected but potentially deadly group of viruses that kills almost 1.4 million people every year.

The five hepatitis viruses cause acute and chronic liver disease. Hepatitis A and E (HAV, HEV) are usually acute, causing symptoms such as jaundice, abdominal pain, vomiting, loss of appetite, fever, and an enlarged liver. Though HAV and HEV are not usually fatal, infection can lead to acute liver failure, which may be deadly. Of the approximately 20 million people who are infected with HEV each year, 3 million develop inflamed livers and 56,600 die. An additional 1.4 million people are infected with HAV every year. These two viruses spread through contaminated food and water and are especially prevalent in rural areas of developing countries, where sanitation may be inadequate.

Hepatitis B and C (HBV, HCV) cause both acute and chronic illness. Acute infection is usually asymptomatic or causes symptoms similar to those of acute HAV and HEV infection. The likelihood that acute HBV infection will lead to chronic illness depends highly on the age of infection: while 80-90% of infected infants will develop chronic infections, under 5% of adults will become chronically infected. HCV, on the other hand, causes chronic infection in 55-85% of infected people. Approximately 240 million people are chronically infected with hepatitis B and 150 million with hepatitis C, yet 70-80% of those infected with hepatitis C do not show symptoms and are likely unaware of their infection. Chronic infection can lead to fibrosis (scarring), cirrhosis, and liver cancer. Together, HBV and HCV are the leading cause of liver cirrhosis and cancer. HBV and HCV are spread by blood, semen, and other bodily fluids. Hepatitis D (HDV) is also spread by bodily fluids, but can only occur in people already infected with HBV.

The WHO released their first set of guidelines for the screening, care, and treatment of HCV-infected individuals this April. In addition, new guidelines for prevention and management of HBV are being developed. On World Hepatitis Day, today, the WHO released a “fact file” on hepatitis E with ten facts about its symptoms, transmission, and modes of prevention.

Vaccines for hepatitis A and B can help protect against infection of these two viruses and hepatitis D. A vaccine for hepatitis E has been developed in China but is not yet available worldwide. Improving sanitation is also an effective measure to prevent hepatitis A and E. No vaccine for hepatitis C is available yet, but research is ongoing. Fortunately, there are some effective drugs used to treat hepatitis C: Sovaldi, produced by Gilead Sciences, has been called a “breakthrough therapy” that can cure patients at up to a 90% rate. However, treatment with Sovaldi is costly—a twelve-week daily dose can cost $84,000—and Gilead has been accused of inflating prices to make a profit.

Certain populations are more at risk for hepatitis infection than others. These groups include but are not limited to people who have lived in or visited endemic areas (see these links for maps of HAV and HBV endemic areas), those who have ever injected drugs, received medical or dental treatment in unsterile conditions, baby boomers (born between 1945 and 1965), people with infected sexual partners, and those with HIV. According to the CDC, about 25% of HIV-infected people are also infected with HCV. To evaluate your own risk, take the CDC’s hepatitis risk assessment.

 

Sources:

http://www.who.int/campaigns/hepatitis-day/2014/en/

http://www.who.int/hiv/pub/hepatitis/hepatitis-c-guidelines/en/

http://www.who.int/features/factfiles/hepatitis-e/en/

http://www.voanews.com/content/australia-hepatitis-drug-costs/1965904.html

http://gamapserver.who.int/mapLibrary/Files/Maps/Global_HepA_ITHRiskMap.png

http://wwwnc.cdc.gov/travel/content/yellowbook/2014/map_3-04.pdf

http://www.cdc.gov/hepatitis/Populations/hiv.htm

http://www.cdc.gov/hepatitis/RiskAssessment/

Digital Disease Detection: Can Wikipedia Monitor Diseases Globally?

Jul 21, 2014 | Nicholas Generous, Reid Priedhorsky, Geoffrey Fairchild and Sara Del Valle | Featured Series

For better or worse, the Internet has become the world’s number one source for health-related information. Think about it: how many times have you Googled symptoms when you were sick? Or how often have you used the Internet to search for answers to questions you think of after visiting a doctor? Because we can pick up and quantify these health information-searching behaviors, it is possible to estimate the levels of disease. Researchers have demonstrated this using search trends from WebMD, Google, Yahoo and most recently Wikipedia.

In a recent Digital Disease Detection post, Dave McIver describes a study that suggested that we could use Wikipedia to track influenza-like-illness (ILI) in the United States by looking at influenza-related page views. But why limit ourselves to tracking flu in the United States or other developed nations? What other diseases can we track using Wikipedia? Where and how can we track them? These novel digital methods of disease detection and monitoring potentially offer the greatest improvements in regions of the world lacking the kind of high-quality ground truth data found in the United States.

Wikipedia, unlike Twitter and Google, makes its complete data freely available for anyone to download. Every hour, access logs containing the number of views each Wikipedia page receives in each language are released. However, unlike Twitter and Google, Wikipedia data does not contain any explicit geo-location information.

Wikipedia data are released and aggregated at the language level. If the geographic distribution of language speakers is mostly clustered in a single location, then one can assume that most of those speakers are in that location. For example, most Thai speakers are in Thailand; therefore, we can assume that data from the Thai Wikipedia are coming mostly from Thailand. The same is true for Polish speakers (concentrated in Poland), and many other language-location pairings.

Another way to geo-locate Wikipedia data is if the disease or outbreak of interest is located in a single location amongst speakers of a particular language. For example, although Portuguese is spoken in many places, the only Portuguese-speaking nation where dengue fever is overwhelmingly prevalent is Brazil. Thus, looking at the Portuguese Wikipedia for dengue can give us a good sense of the dengue activity in Brazil.

To see if we could track disease levels around the world using the aforementioned methods, we downloaded the entire history of page views for seven different language Wikipedia—Portuguese (Brazil), Chinese (China), Japanese (Japan), Polish (Poland), Norwegian (Norway), Thai (Thailand) and English (United States)—and built models from time series of disease-related Wikipedia pages. We trained linear models for influenza, dengue, tuberculosis, HIV/AIDS, bubonic plague, cholera, and Ebola in nine countries using the official data from each nation’s respective governmental health organization (e.g., CDC, Thai Ministry of Health).

So, how well did our models perform at monitoring diseases around the world? All our flu and dengue models did well; our flu models were successfully able to track flu in Poland, Thailand, Japan, and the United States with high accuracy (the model fit (r2) ranged from 0.80 to 0.92, where 1 is best). We found our success in the United States surprising, given that English is spoken all over the world. Similarly, our dengue models for Brazil and Thailand performed well (r2 of 0.86 and 0.74, respectively). Of the three tuberculosis models we built, the Chinese and Thai models showed promise (r2 of 0.78 and 0.69, respectively) whereas the Norwegian one did not (r2 of 0.48).

Perhaps our model failures are more interesting than our model successes. We had less success with our HIV/AIDS, plague, cholera, and Ebola models. For the models of plague in the United States, Ebola in Uganda/Democratic Republic of Congo, and cholera in Haiti, we suspect that the number of page views of the disease-related pages drown out the actual observations of the disease. In the cases of plague and Ebola, these diseases are widely known by people but are extremely rare (e.g., plague has less than a handful of cases in the United States in the previous few years). Furthermore, especially in the cases of cholera in Haiti and Ebola in Africa, these outbreaks occur in regions with low Internet penetration, further limiting the chances that direct observations are being logged by Wikipedia. Our HIV/AIDS models in China and Japan faced a different problem—the disease incubation period is so long (years, even decades) that the variation we observed in the official data likely does not accurately reflect the true incidence of the diseases.

What happens when we do not have “gold standard” data by which we can build and validate a model? This is a very real concern in many developing nations where the health infrastructure does not have sufficient resources to accurately monitor diseases. It is precisely in these cases where digital disease detection methods can potentially be transformative since it would allow us to cheaply and sustainably track diseases in regions that have minimal public health surveillance.

To assess this, we wanted to see if any of our models are transferable to different locations without having to train them using “gold standard” data. Surely, people in differing locations behave similarly when they get sick and will visit the same types of Wikipedia pages. If that is indeed the case, then it may be possible to train a model in one country where we have good ground truth data and use that model in another country that does not have trustworthy ground truth data. By looking at whether the page views from the same Wikipedia pages across different languages correlated with disease levels, it is possible to get a sense of whether we can transfer models from one location to another. For example, if people in the United States, Thailand, and Poland all look at the same Wikipedia page when sick and you can use this page to infer flu levels, then it is conceivable that you can do this in other countries as well without building a new model. Among our flu models, we found that transferability of models may be possible.

Wikipedia, one of the Internet’s most popular websites, is an exciting and novel data source that shows extreme potential for disease surveillance worldwide. Unlike other Internet data sources such as Google, Twitter, or Facebook, which may release a limited dataset free of charge, complete Wikipedia data are available freely to anyone. This is an extremely important fact if we are to further develop Wikipedia data into an operational disease monitoring system. While Wikipedia is currently limited because its data do not contain any explicit geo-location information (i.e., where are the page views coming form), this can be easily fixed by the Wikimedia Foundation, which could release the data aggregated not only by language, but also by geographical location. Maybe then we will be able to develop a Wiki Flu trends!

For more information, see our pre-print article, Global disease monitoring and forecasting with Wikipedia at http://arxiv.org/abs/1405.3612

 

Bios:

Nick Generous is a post-masters research associate at Los Alamos National Laboratory. His work focuses on Internet disease detection and operation tools for biosurveillance.

Reid Priedhorsky is a postdoctoral research associate at Los Alamos National Laboratory. His work focuses on large-scale data analysis and collaborative computing, with a focus on empowering communities to make better decisions in pursuit of a sustainable and just global future.

Geoffrey Fairchild is a computer science Ph.D. student at the University of Iowa and a graduate research assistant at the Los Alamos National Laboratory. His research applies computer science, geographic information systems, and mathematics to epidemiological problems in order to simulate, analyze, and predict disease spread. 

Sara Del Valle is a scientist and project leader at Los Alamos National Laboratory. Her research focuses on developing mathematical and computational models for mitigating the spread of infectious diseases with a special interest in using social media to model and forecast human behavior.

Polio: What Happens to a Vaccine-Preventable Disease When Vaccination Is Prevented?

Jul 18, 2014 | Alexandra Thomsen | Commentary

Polio is a highly infectious disease that mainly affects children under age five and leads to irreversible paralysis in about one of every 200 cases. The first polio vaccine was developed in 1952 by Jonas Salk. Today, there are two forms of the vaccine: inactivated polio vaccine (IPV) and oral polio vaccine (OPV). OPV can be administered by anyone, including volunteers, and can cost as little as 11 U.S. cents. Although polio used to be a global threat, its prevalence has been reduced by over 99% since formation of the Global Polio Eradication Initiative in 1988. However, despite the existence of vaccines, polio is still endemic in three countries: Afghanistan, Nigeria, and Pakistan. Poor sanitation—including lack of access to toilets and safe drinking water—is often cited as a factor that facilitates the spread of polio because the virus is transmitted through the fecal-oral route. However, another major obstacle to polio eradication is the opposition to vaccination.

Vaccination may be opposed for a variety of reasons, including religious reasons, mistrust, and misconceptions about what the vaccines do. For example, in 2003-2004 in Nigeria, rumors were spread that the polio vaccine was sterilizing children as a form of population control and contained HIV. In Pakistan, similar rumors have surfaced in addition to some claims that the vaccines contain pork (which, for the country’s large Muslim population, is non-halal). In addition, in poor regions of these countries, the lack of basic necessities like water and electricity has caused communities to question government spending on polio vaccines. Other times, vaccination may be prevented by social or political unrest that make it difficult and unsafe for vaccination campaigns to take place. The latter reason has been the case in both Nigeria and the Federally Administered Tribal Areas of Pakistan.

Turmoil caused by the Boko Haram insurgency has impeded polio eradication efforts in the northern states of Nigeria, where polio transmission is ongoing and vaccination campaigns are most needed. Boko Haram has become well known by the international community in recent months following the kidnapping of hundreds of schoolgirls in April this year. The name “Boko Haram” means “Western education is forbidden,” and the group also opposes Western-sponsored polio vaccination campaigns. One notable attack on healthcare workers in Nigeria occurred in February 2013, when nine women were targeted by gunmen for working on a polio vaccination campaign. Nigeria has had five polio cases this year in the northern states of Kano and Yobe, the most recent of which was reported in Kano state in the past week. Aid workers have been implementing a “hit and run” strategy to vaccinate unsafe areas by sending mobile units in and out as quickly as possible.

In Pakistan, polio cases are heavily concentrated in the Federally Administered Tribal Areas (FATA), where militancy and unrest are also prevalent. This June, a Pakistani Taliban commander banned polio vaccines in North Waziristan, a region in FATA responsible for 63% of the country’s cases this year. The Pakistani Taliban and connected militant groups oppose vaccines in part because the U.S. used a polio vaccination campaign as a cover to facilitate the search for Osama bin Laden in 2011. That operation outraged many in the public health community who feared the deception would increase the danger to polio workers (for more information, see http://blogs.plos.org/speakingofmedicine/2011/09/12/failed-vaccine-campaigns-are-a-global-issue/). On July 7, the Afghan Taliban also banned polio vaccinations in a southern province of Afghanistan. As in Nigeria, polio workers have been targeted for carrying out vaccination campaigns. Eleven people were killed in Khyber Agency on March 1, 2014 when a roadside bomb hit vans carrying polio vaccination workers. Later in March, a Pakistani polio worker in Peshawar was kidnapped and killed—supposedly because of her work. Despite the dangers and opposition that polio workers have faced, about 2.5 million Pakistani children were vaccinated in Khyber-Pakhtunkhwa and tribal regions last month as part of a three-month campaign. This initiative was launched by the UAE Pakistan Assistance Programme (UAE PAP) and was effective because it was led by the UAE in cooperation with Pakistan’s army and ministry of health.

Pakistan has had 94 polio cases this year—more than any other country in 2014. According to a Global Polio Eradication Initiative update as of July 15, Afghanistan is next with eight cases, followed by Equatorial Guinea and Nigeria with five each. Somalia has had four cases, Cameroon has had three, Iraq has had two, and Syria and Ethiopia have had one case each. Wild poliovirus discovered in an environmental sample in São Paolo, Brazil was found to be genetically similar to the strain in Equatorial Guinea.

Ebola Update: Largest Outbreak on Record Causes 603 Deaths

Jul 16, 2014 | Alexandra Thomsen | Outbreak News

On July 15, the WHO reported a total of 964 cases including 603 deaths in Guinea, Sierra Leone, and Liberia. Guinea still has the greatest number of cases with 406 confirmed, probable, and suspected cases, including 304 deaths. Sierra Leone is next with 386 cases and 194 deaths, and Liberia follows with 172 cases and 105 deaths. While Guinea’s outbreak seems to be slowing down, Sierra Leone’s continues to grow rapidly—of the 68 new deaths reported, 52 were in Sierra Leone, 13 in Liberia, and only three in Guinea.

Fear of Ebola has caused other nations, including Nigeria, to educate citizens on Ebola and implement preventive measures. Fear has also led the Ivory Coast to refuse entry to around 400 refugees attempting to return to their home country from Liberia. In Accra, Ghana, a U.S. citizen was quarantined under suspicions of Ebola, but initial tests were negative for the disease.

This outbreak has been particularly hard to contain because of a lack of sufficient protective equipment for healthcare workers, mistrust of Western medicine, frequent cross-border movement, and traditional practices that unintentionally spread the disease. Many infected individuals refuse to go to the hospital because they fear stigmatization or they believe that hospitalization is a “death sentence.” In Liberia, Chief Medical Officer Dr. Bernice Dahn reports that some individuals seek treatment in churches rather than hospitals because they believe the disease is a spiritual ailment. Hostility towards medical workers in all three countries has also barred treatment. In one such incident, locals wielding knives surrounded a Red Cross vehicle in Gueckedou, Guinea, leading the organization to suspend operations at their treatment center there.

This outbreak, which began in March 2014 and is still ongoing, is the largest on record in terms of the number of cases, deaths, and countries affected. Previously, the largest Ebola outbreak had been in Uganda in 2000-2001, when 425 people were affected and 224 died (see the CDC’s Ebola outbreak table for more information on this and other outbreaks). During the current outbreak in West Africa, 664 cases have been confirmed of the 964 reported cases. Though the case fatality rate of Ebola—the percentage of cases that result in death—is usually reported as 90%, this figure is based on the first recorded outbreak in Zaire in 1976 and is significantly higher than the case fatality rate of the current outbreak. Guinea’s case fatality rate (including suspected, probable, and confirmed cases and deaths) is about 75%, Sierra Leone’s is 50%, and Liberia’s is 61%. Combined, the three countries have a case fatality rate of 62.6%.

 

Sources:
http://www.who.int/csr/don/2014_07_15_ebola/en/

http://www.dailytimes.com.ng/article/lagos-alerts-public-ebola-virus

http://medicalxpress.com/news/2014-07-ivorian-refugees-home-ebola.html

http://www.ibtimes.co.uk/west-africa-ebola-outbreak-tests-show-us-citizen-ghana-not-infected-deadly-virus-1455745

http://uk.reuters.com/article/2014/07/13/us-health-ebola-westafrica-idUKKBN0FI0P520140713

http://allafrica.com/stories/201406270789.html

http://www.trust.org/item/20140702083611-3yofq

http://www.cdc.gov/vhf/ebola/resources/outbreak-table.html

Just the Vax, Please: Six Signs the Article You’re Reading is Bad Science

Jul 16, 2014 | Jane Huston, Robyn Correll Carlyle | Featured Series

One reason we feel so strongly about the role of vaccines in public health is because of the massive amount of good data and quality studies that support them. And that’s why it’s frustrating when media, the Twittersphere, or the internet in general circulate rumors and poorly designed studies attacking vaccines.

The thing is-- science is hard. Like, really hard. People study for up to 10 years to be a qualified researcher. And reading scientific literature can be a bit tricky. We can’t catch you up to the guy or gal who’s devoted an entire career to vaccinology or epidemiology, but here’s a handy cheat sheet to help you spot the “junk science” when it comes across your news feed:

1. It confuses correlation and causation.

This is a big one, and possibly a mantra you’ve heard before: correlation does not equal causation. Correlation is a statistical term that simply means the way two variables fluctuate appear to be related in some fashion. Any fashion. Maybe variable A going up happens at approximately the same rate as variable B going down. Maybe they increase together. Maybe it’s not a linear relationship (but that’s a bit more complicated). What’s most important to remember here is we absolutely cannot assume that one is causing the other. We simply don’t have enough information since all we know is how the variables are changing.

Take this example of Nicolas Cage films and drowning deaths. That plot looks pretty good right? And the correlation coefficient is a fairly solid 0.66. Could it be that Cage’s action-packed thrillers are inexplicably driving people towards backyard swimming pools? Anything’s possible. But the two almost certainly have nothing to do with each other and are, instead, a total coincidence. Often when two variables are correlated, there is actually an unknown third (and potentially fourth and fifth) variable that is affecting both of the events you’re examining.

2. Its sample size is small.

People suffer from a wide range of medical issues every day -- sometimes they are caused by what you’re studying, but sometimes it’s just by chance that the participants being studied develop an issue. Out of a study sample of three, having one guy get hit by a bus would look like a significant trend. The larger the sample size, the less impact those random occurrences will have on your data.

3. The study is uncontrolled.

Not uncontrollable like your two-year-old nephew on a sugar-high, but uncontrolled as in lacking a control group. A control group provides a researcher something to which to compare results; it’s the closest way to estimate the counterfactual. Did the subjects get better over the course of the experiment because of a drug being tested, or would they have improved anyway? A control group that is similar to the experimental group in every way EXCEPT for the intervention can help you answer that question.

4. The results are not replicable.

One study alone (even a well designed, large-scale one) can’t prove anything. All it can do is contribute to the body of work already done by the scientific community. It takes several studies coming to the same conclusion to say anything with confidence -- and even then we can’t be 100% certain. Science is purposefully self-correcting. Researchers rely on each other to validate their results. If no other researchers have been able to replicate a study’s findings, that’s a red flag.

On a related note, beware of those researchers who are only citing themselves. If an author says that there is “substantial evidence to support” a given link or a particular cause, check out the citations. Have several different research groups provided evidence to support the link? Or is it just one name (the author’s) that keeps popping up? If that author is the only one who seems to be providing that “substantial evidence,” it’s worth taking with a fistful of salt.

5. There’s a conflict of interest.

This is a sensitive but important point. When publishing a paper, authors must disclose the source of funding for their work as well as any other relevant conflicts of interest, such as ownership of a related private company. This does not necessarily invalidate the results of the experiment, but you should definitely be aware of any potential bias when reading results. If the author has a lot to gain from the study and the results seem glowing with no down-sides or limitations, be suspicious.

6. It’s published in a journal that’s not peer-reviewed.

Whenever possible, try to read the original journal article instead of relying on the popular press. Articles in general news media can be a great source to find out about new and interesting research, but remember they are necessarily interpreted by a reporter (in best cases by a science writer with a background in science; in the worst cases it’s a press release). While you’re reading the original article, make a note of the journal it appears in. Is it a reputable publication, like Nature, Journal of American Medical Association or the New England Journal of Medicine? Did articles have to pass a peer-review process, meaning that other experts read the manuscript, asked probing questions, pointed out any errors, and addressed limitations? This process is by no means perfect; mistakes can certainly still get through peer review and show up in reputable sources. But on the whole, a study appearing in a respected, peer-reviewed academic journal carries more weight than one published on a personal blog.

There’s another deadly threat -- single-issue shill “journals” published entirely to push an agenda. This is the worst possible abuse of the scientific process. Some people, after being spurned by reputable journals, will go so far as to create their own journals to fabricate a veneer of legitimacy for their flawed ideas. These biased publications are a wolf in sheep’s clothing. Avoid them at all cost.

In the age of the internet, it’s getting harder to tell good science from bad. But if you follow this guide, and approach scientific articles with a healthy dose of skepticism, you’ll do fine.

 

Summer: Time for Influenza Pandemics?

Jul 1, 2014 | Alexandra Thomsen | Commentary

In the U.S. and the rest of the Northern Hemisphere, the flu is usually considered a wintertime illness. However, some of the most significant influenza pandemics of the past century have had initial peaks in the summer. Among them is the Spanish flu pandemic of 1918-1919 (the “mother of all” influenza pandemics), which emerged in the U.S. in March 1918 and spread across Europe in May and June. The more recent swine flu (H1N1) pandemic in 2009 also began in the spring and reached pandemic levels by June. Another notable pandemic is the Hong Kong flu (H3N2), which emerged in July 1968 and peaked after only two weeks. Because these influenza pandemics did not emerge during the typical “flu season,” they highlight the importance of maintaining flu surveillance during the off-season.

The difference between a regular flu season and a pandemic is that influenza pandemics exhibit explosive transmission and high morbidity but low mortality (meaning that although many people may die, the deaths only represent a small proportion of the much larger total who fall ill). All known influenza pandemics in human history have been caused by different subtypes of the influenza A virus (the other virus types are influenza B and C). The influenza A subtypes that have historically caused human disease are three HA subtypes (H1, H2, and H3) and two NA subtypes (N1 and N2). For example, the Spanish flu of 1918 was H1N1, the Asian flu of 1957 was H2N2, and the Hong Kong flu of 1968 was H3N2. More recently, the H5, H7, and H9 subtypes of HA have been discovered to cause human disease.

Avian influenza A (H7N9) had been found only in birds until March 2013, when human cases were discovered in China. Since then, there have been 429 H7N9 cases including at least 100 deaths. Human to human transmission of the disease remains limited, and most human cases result from contact with infected poultry or contaminated environments. Although it emerged in the spring of 2013, this strain of influenza did not exhibit an initial summertime peak and has not reached pandemic levels. However, an increase in infections since October 2013 may indicate a seasonal pattern similar to that of highly pathogenic influenza A (H5N1), in which cases are most common in winter months. H5N1 has infected 665 people including 392 deaths since its emergence in humans in 2003.

Although the H7N9 and H5N1 avian influenza strains have not caused pandemics or infected significant numbers of people during the summer, the risk of a new influenza strain emerging and causing a summertime pandemic is not too far-fetched—just look at the history. Influenza outbreaks are unpredictable, and two or more influenza strains can combine to form a new strain at any time, whether it's the flu season or the off-season.

If you are interested in participating in a year-round citizen science influenza surveillance project, check out Flu Near You (flunearyou,org). Its year-round surveillance can provide an early warning signal if another pandemic emerges.

 

Sources:

http://contagions.wordpress.com/2010/12/31/pandemic-influenza-1510-2010/

http://origins.osu.edu/article/influenza-pandemics-now-then-and-again

http://www.cidrap.umn.edu/infectious-disease-topics/pandemic-influenza

http://www.rapidreferenceinfluenza.com/chapter/B978-0-7234-3433-7.50010-4/aim/influenza-pandemics-of-the-past

http://www.who.int/influenza/human_animal_interface/influenza_h7n9/140225_H7N9RA_for_web_20140306FM.pdf?ua=1

http://www.asianscientist.com/topnews/map-pinpoints-bird-flu-risks-asia-2014/

Wild Poliovirus Type 1 Found in Brazil Sewage Samples – What Does It Mean?

Jun 24, 2014 | Alexandra Thomsen | Outbreak News

Environmental samples from Campinas, São Paolo have tested positive for wild poliovirus type 1 (WPV1), though the country has not reported any polio cases since 1989. The samples were collected in March 2014 from the International Airport of Viracopos in Campinas as part of routine environmental surveillance. On June 18, the Brazil International Health Regulations (IHR) National Focal Point (NFP) reported WPV1 detection. This strain of poliovirus is genetically similar to a strain isolated from Equatorial Guinea, where four cases have been reported this year (as of June 17, according to the Global Polio Eradication Initiative).

To be clear, detection of WPV1 in environmental samples does not mean that there are polio cases in Brazil or that polio will reemerge in the country. WPV1 was detected in sewage samples in Israel in April 2013 and no cases have been reported following environmental detection of the virus. To avoid the risk of polio reemergence, Israel launched a large-scale vaccination campaign. Brazil has been conducting national vaccination campaigns since the 1980s; Campinas and the state of São Paolo have vaccination coverage of over 95 percent. This high level of immunity in the population means that the virus is unlikely to be transmitted among the population. However, PAHO/WHO (the Pan-American Health Organization/World Health Organization) has urged Member States to heighten surveillance for acute flaccid paralysis, which is a sudden onset of abnormal weakness and paralysis often associated with polio.

A common misconception is that detection of acute flaccid paralysis (AFP) in a population indicates the presence of polio cases. However, it is only the detection of abnormal levels of AFP that should raise concerns about polio. AFP is associated with a number of other syndromes and disorders, including Guillain-Barré syndrome, transverse myelitis, enteroviral encephalopathy, traumatic neuritis, Reye’s syndrome, and more.

Less than 1 percent of polio cases exhibit permanent paralysis, according to the CDC. About 72 percent of cases have no symptoms, while 24 percent have minor symptoms (fever, fatigue, nausea, headache, flu-like symptoms, neck and back stiffness, and limb pains). Death can occur if paralysis reaches the respiratory muscles and is more likely in older individuals. Poliovirus is spread from person to person by fecal-oral contamination, which involves contact with infected mucus, phlegm, feces, or contaminated food and water. Because there is no cure for polio, vaccination is crucial.

 

Sources

http://www.paho.org/hq/index.php?option=com_docman&task=doc_view&gid=25922+&Itemid=999999&lang=pt

http://www.polioeradication.org/Dataandmonitoring/Poliothisweek.aspx

http://www.who.int/csr/don/2013_06_03/en/

http://healthmap.org/site/diseasedaily/article/israel-launches-polio-vaccination-campaign-8913

http://en.wikipedia.org/wiki/Flaccid_paralysis

http://www.cdc.gov/vaccines/vpd-vac/polio/in-short-both.htm

Digital Disease Detection: We See The Trends, But Who is Actually Sick?

Jun 23, 2014 | Elaine Nsoesie | Featured Series

There are plenty of studies about tracking diseases (such as influenza) using digital data sources, which is awesome! However, many of these studies focus solely on matching the trends in the digital data sources (for example, searches on disease-related terms, or how frequently certain disease-related terms are mentioned on social media over time, etc.) to data from official sources such as the Centers for Disease Control and Prevention. Although this approach is useful in telling us about the possible utility of these data, there are several limitations. One of the main limitations is the difficulty in distinguishing between data generated by healthy individuals and individuals who are actually sick. In other words, how can we tell whether someone who searches Google or Wikipedia for influenza is sick or just curious about the flu?

Researchers at Penn State University have developed a system that seeks to deal with this limitation. We spoke to the lead author, Todd Bodnar, about the study titled, On the Ground Validation of Online Diagnosis with Twitter and Medical Records. According to Bodnar, the study was born from a desire to “approach digital disease detection from a new angle” given the recent criticism of the reliability of Google Flu Trends (GFT). He and his co-authors were interested in focusing on whether people are sick or not, rather than the average rate of influenza in the population.

In the paper, they present a novel approach for disease detection at the individual level using social media data. “We started with data from people that we knew were actually sick. We collaborated with our university's health services to find people that were diagnosed with influenza. From there, we took their twitter data and tried to develop an automated diagnosis system that matched the doctor's diagnosis,” writes Bodnar in an email correspondence.

In this initial study, 104 twitter “seed” (meaning the initial or primary accounts being examined) accounts were included. The authors collected 37,599 tweets from these accounts in addition to 30,950,958 tweets from accounts of individuals that were followed by or that followed the seed accounts. To classify individuals as “sick” or “not sick” using these data, they developed classification schemes based on the presence or absence of flu-related keywords (flu, influenza, sick, cough, cold, medicine, fever); manual labeling of tweets based on hints indicating illness (such as “another doctor’s appointment…”); the rate at which individuals tweet, since illness can influence changes in tweeting behavior; and analysis of tweets by individuals that were followed by or that followed the seed accounts. In total, the researchers used five methods for detecting whether an individual was sick.

So what did they find?  Bodnar states that, “about half of the active Twitter users we surveyed actually discussed being sick on Twitter. We were able to diagnose the other half accurately by data-mining more subtle clues from their Twitter stream. For example, if someone says that she's going to a party, she's probably not sick. On the other hand, a reduction of tweeting rates by more than one standard deviation results in a 28.54 percent increase in likelihood of illness.” Bodnar also says that, “the system matched the professional diagnosis more than 99 percent of the time.” Basically, the authors show that by using social media data (specifically tweets), they can tell whether can individual is actually sick or not.

Obviously there are several ethical and technical challenges to doing a study like this since it involves personal data, which can be very sensitive. (Check out this recent DDD article, One Researcher’s Take on Twitter, Research and Privacy, for a discussion of some of the ethical issues in the field.) The researchers had to submit their study proposal to what is called an IRB, or an institutional review board. Institutions that conduct research generally have a board of individuals who review research proposals and determine whether or not they are ethically sound.

“We're actually planning on applying this to HIV in the future. It's a more complex problem. We probably couldn't diagnose people that weren't aware that they're HIV-positive, but could use it as a stepping point for looking at other behaviors such as promiscuity or anti-retroviral usage,” writes Bodnar. The authors are also working on applying this method on a larger scale.

And for those of you who are students and interested in Digital Disease Detection, Bodnar advises: “Be interested in listening to whispers in the data, but at the same time, don't look at the moon and claim to see a face!”

Update on Chikungunya in the Americas

Jun 18, 2014 | Alexandra Thomsen | Outbreak News

Chikungunya, a mosquito-borne viral illness found for the first time in the Americas in December last year, has been spreading throughout Caribbean territories and continues to be reported in new countries and territories in the Americas. The Ministry of Health of El Salvador reported 1200 cases of chikungunya on June 14—the first reported cases in the nation. The CDC has reported 57 imported cases in the U.S., but no locally transmitted cases have been discovered within the continental U.S. thus far. However, the U.S. Virgin Islands confirmed its first locally transmitted case of chikungunya on June 11, after Puerto Rico reported the first non-imported case in U.S. territory on May 29. The CDC currently reports 23 locally transmitted cases in Puerto Rico. Meanwhile, the virus continues to spread rapidly in other territories such as Guadeloupe (an overseas territory of France), where 5190 new cases were reported between May 26 and June 1. That amounts to 1 new case every 2 minutes.

The first locally contracted cases of chikungunya in the Americas were found on the French side of St. Martin in December. Previously, the virus had been most common in Africa and Asia. In an update on June 13, the Pan American Health Organization (PAHO) reported 165,990 suspected and confirmed cases in the Americas since the beginning of the outbreak. Countries and territories with especially high incidence rates include St. Martin (French side), Martinique, St. Barthelemy, Guadeloupe, and Dominica. The autochthonous (non-imported) cases in Puerto Rico pose a greater threat to the U.S. than cases elsewhere in the Americas because of the frequency of travel between the mainland U.S. and Puerto Rico.

The emergence of locally transmitted chikungunya cases in Puerto Rico is a cause of public health concern because of the interconnectedness between Puerto Rico and the continental U.S. The virus threatens to become introduced to the U.S. via frequent air travel between Puerto Rico and the mainland. To illustrate this frequency, consider the number of Puerto Ricans immigrating to New York City alone: the Puerto Rican population in NYC experienced an increase of 24,420 people between 2010 and 2012—an average of 235 people per week! The most vulnerable region of the U.S., however, is the southeast, due to its proximity to the Caribbean and the prevalence of Aedes aegypti mosquitoes, which have been spreading the disease throughout the Caribbean. Other regions of the U.S. that have reported imported cases are not at such a high risk for local transmission because of the absence of Aedes aegypti mosquitoes in more northern regions.

Chikungunya causes fever and severe joint pain, which explains the meaning of the name in the Kimakonde language: “to become contorted [with pain].” The disease also often causes muscle pain, headache, nausea, fatigue, and rash. Although serious complications are not common, debilitating joint pain can last for days, weeks, or even months. In older individuals, the disease may lead to death. There is no cure or vaccine for chikungunya, but treatment aims to ameliorate the symptoms. Because the disease is mosquito-borne, like dengue and malaria, communities can lessen the likelihood of an outbreak by removing any possible mosquito breeding grounds, such as plant pots and old tires filled with stagnant water.

 

Sources:

http://hoy.com.do/confirman-mil-200-casos-del-virus-chikungunya-en-el-salvador/

http://www.examiner.com/article/u-s-chikungunya-cases-more-than-double-one-week?cid=rss

http://www.mynews4.com/news/story/US-Virgin-Islands-confirms-first-chikungunya-case/Ki8VdztZGkeZOgx0b3dmXw.cspx

http://www.jamaicaobserver.com/latestnews/Caribbean-records-first-case-of-chikungunya

https://news.yahoo.com/puerto-rico-confirms-1st-chikungunya-case-232022610.html

http://www.domactu.com/actualite/146825344594676/guadeloupe-chikungunya-1-contamination-toutes-les-2-minutes/

http://www.who.int/mediacentre/factsheets/fs327/en/

http://www.paho.org/hq/index.php?option=com_docman&task=doc_view&gid=index.php?option=com_docman&task=doc_download&gid=25866&Itemid=

http://en.wikipedia.org/wiki/Puerto_Rican_migration_to_New_York_City#cite_note-2012PRest-6

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