Coronavirus (Covid-19),Africa and Beyond By Brian Simpson

     The White House is highly suspicious about China’s official statistics on the number of people infected with coronavirus (Covid-19), indicating that China is lying, something that American Renaissance magazine should consider the significance of, given their East Asian superiority thesis. Anyway, Trumpery and co are likely to be right about this because Covid-19 is more infectious than has been previously thought:
  https://www.zerohedge.com/health/chinas-coronavirus-numbers-dont-add-and-white-house-doesnt-believe-them
  https://www.shtfplan.com/headline-news/trump-does-not-trust-the-coronavirus-numbers-that-are-coming-out-of-china_02172020
  https://www.naturalnews.com/2020-02-17-new-study-novel-coronavirus-r0-value-high.html

“Researchers from the Los Alamos National Laboratory in New Mexico have pre-published a new study in the journal medRxiv (associated with the British Medical Journal) that says the actual basic reproductive number, or R0, for novel coronavirus is somewhere between 4.7 and 6.6, as opposed to the 2.2 to 2.7 range being claimed by government health authorities. While the study has not yet been peer-reviewed as of this writing, its abstract explains that “extensive individual case reports” on this constantly evolving global outbreak reveal that the R0 estimates put forth by those “in charge” are essentially flawed. After evaluating these case reports and assessing them alongside estimated key epidemiology parameters, including the virus’ incubation period – we now know the incubation period to be closer to a month as opposed to just two weeks – the team determined that each coronavirus-infected person can potentially infect nearly seven others with the disease. “Integrating these estimates and high-resolution real-time human travel and infection data with mathematical models, we estimated that the number of infected individuals during early epidemic double every 2.4 days, and the R0 value is likely to be between 4.7 and 6.6,” the paper explains.”

     If this is so, Billy at the Gates is probably right to see Africa being hard hit, with up to 10 million deaths, once Covid-19 really gets down to it. But, that is pessimistic compared to a Los Alamos expert who predicts only 4.4 million deaths:
  https://www.telegraph.co.uk/news/2020/02/15/coronovirus-bill-gates-warns-10-million-deaths-virus-spreads/
  https://www.zerohedge.com/technology/los-alamos-experts-warn-covid-19-almost-certainly-cannot-be-contained-project-44-million

“The Covid-19 outbreak in China is quickly spreading worldwide, sparking quick calculations on how deadly this new disease is. One measure is called a case fatality rate. While the formula is simple, it’s difficult to get a precise answer.HYACINTH EMPINADO/STAT. The computers that run disease models grind through calculations that reflect researchers’ best estimates of factors that two Scottish researchers identified a century ago as shaping the course of an outbreak: how many people are susceptible, how many are infectious, and how many are recovered (or dead) and presumably immune. That sounds simple, but errors in any of those estimates can send a model wildly off course. In the autumn of 2014, modelers at CDC projected that the Ebola outbreak in West Africa could reach 550,000 to 1.4 million cases in Liberia and Sierra Leone by late January if nothing changed. As it happened, heroic efforts to isolate patients, trace contacts, and stop unsafe burial practices kept the number of cases to 28,600 (and 11,325 deaths). To calculate how people move from “susceptible” to “infectious” to “recovered,” modelers write equations that include such factors as the number of secondary infections each infected person typically causes and how long it takes from when one person gets sick to when the people she infects does. “These two numbers define the growth rate of an epidemic,” Vespignani said.

The first number is called the basic reproduction number. Written R0 (“R naught”), it varies by virus; a strain that spreads more easily through the air, as by aerosols rather than heavier droplets released when an infected person sneezes or coughs, has a higher R0. It has been a central focus of infectious disease experts in the current outbreak because a value above 1 portends sustained transmission. When the R0 of Covid-19 was estimated several weeks ago to be above 2, social media exploded with “pandemic is coming!” hysteria. But while important, worshipping at the shrine of R0 “belies the complexity that two different pathogens can exhibit, even when they have the same R0,” the Canadian-U.S. team argues in a paper posted to the preprint site medRxiv. Said senior author Antoine Allard of Laval University in Quebec, “the relation between R0, the risk of an epidemic, and its potential size becomes less straightforward, and sometimes counterintuitive in more realistic models.” To make models more realistic, he and his colleagues argue, they should abandon the simplistic assumption that everyone has the same likelihood of getting sick from Covid-19 after coming in contact with someone already infected. For SARS, for instance, that likelihood clearly varied. “Bodies may react differently to an infection, which in turn can facilitate or inhibit the transmission of the pathogen to others,” Allard said. “The behavioral component is also very important. Can you afford to stay at home a few days or do you go to work even if you are sick? How many people do you meet every day? Do you live alone? Do you commute by car or public transportation?”

When people’s chances of becoming infected vary, an outbreak is more likely to be eventually contained (by tracing contacts and isolating cases); it might reach a cumulative 550,000 cases in Wuhan, Allard and his colleagues concluded. If everyone has the same chance, as with flu (absent vaccination), the probability of containment is significantly lower and could reach 4.4 million there. Or as the researchers warn, “the outbreak almost certainly cannot be contained and we must prepare for a pandemic ….” Modelers are also incorporating the time between when one person becomes ill and someone she infects does. If every case infects two people and that takes two days, then the epidemic doubles every two days. If every case infects two people and they get sick four days after the first, then the epidemic doubles every four days. This “serial time” is related to how quickly a virus multiplies, and it can have a big effect. For a study published this month in Annals of Internal Medicine, researchers at the University of Toronto created an interactive tool that instantly updates projections based on different values of R0 and serial interval. Using an R0 of 2.3 and serial interval of seven days, they project 300,000 cases by next week. If the serial interval is even one day less, the number of cases blasts past 1.5 million by then. But if the countermeasures that China introduced in January, including isolating patients, encouraging people to wear face masks, and of course quarantining Wuhan, reduce the effective reproduction number, as has almost certainly happened, those astronomical numbers would plummet: to 100,000 and 350,000 cases, respectively.

Just as public health officials care how long someone can be infected without showing symptoms (so they know how long to monitor people), so do modelers. “When people are exposed but not infected, they tend to travel and can’t be detected,” Vespignani said. “The more realistic you want your model to be, the more you should incorporate” the exposed-but-not-ill population. This “E” has lately become a fourth category in disease models, joining susceptible, infectious, and recovered. At Los Alamos, Del Valle and her colleagues are using alternatives to the century-old susceptible/infectious/recovered models in hopes of getting a more realistic picture of an outbreak’s likely course. A bedrock assumption of the traditional models is “homogeneous mixing,” Del Valle said, meaning everyone has an equal chance of encountering anyone. That isn’t what happens in the real world, where people are more likely to encounter others of similar income, education, age, and even religion (church pews can get crowded).”

     However, the above  prediction of 4.4 million dead is based upon a RO of 2.3, but the actual RO could be much higher as the above articles probe. At this stage it is anyone’s guess what will happen here, but there is no question that the epidemic is severely impacting upon China. It is possible that the main effects to be felt in the West will be on a decrease in Chinese trade. This will surely reveal the utter folly of globalism, but the elites relentless pursue it, because, well, they must be genetically programmed to do so.