...TUESDAY APRIL 14 2020 2:00 PM EDT... The ongoing global outbreak of the COVID-19 coronavirus is unlike anything we have seen in modern history. It is a highly contagious disease with severe symptoms in several of its cases with no current scientifically established cures or vaccines, so all you can do if infected is to ride it out at home or seek treatment at hospitals if symptoms worsen. If the symptoms are severe enough and the treatments do not work, or you do not have access to needed treatment due to a shortage of medical supplies at hospitals, you lose your life. A significant number of fatalities have occurred, already topping 125,000 worldwide (https://www.worldometers.info/coronavirus/). The reality is from today's perspective that COVID-19 fatalities are going to increase as this highly contagious disease is still not contained from the public in several nations.
However there is some hope beginning to show in the latest data. For certain nations, including hard-hit United States and Italy, I have been collecting data on the numbers of coronavirus cases from various sources outlined below. The data shows we are beginning to see a slow down in the new number of cases per day even in hard-hit areas. And that appears to be the effects of staying at home whenever possible, social distancing in public (maintaining a physical distance from each, and government actions such as travel shutdowns and orders that re-enforce social distancing through limits on gatherings, closing schools, non-essential business, etc. So if you have been under government or business orders to stay at home and socially distance when you have to be in public, and are wondering if there is anything meaningful happening, the data below should be encouraging, so lets continue so that the disease can be contained. If you are not under any orders, try to stay at home as much as possible and socially distance in public. And for everyone under orders or not, don't forget about sanitation habits such as keeping your hands clean by old fashioned washing with soap and water or using disinfectant gels or wipes, and having a holding area in your home to temporarily place items ordered online or picked up from any store where you can disinfect their packaging (bags, boxes, cans, etc.). You can also use this holding area to give time for any bacteria to dissipate on the packaging before storing bought items in your home.
...WHAT CAN WE DO IN FUTURE PANDEMICS?...
For the future once we get beyond COVID-19, I hope we re-think how we handle
future disease pandemics, in particular the highly-contagious kind like COVID-19.
The fact is that nations that had aggressive government action early on, such as
China and South Korea, have already contained the disease and did so more
quickly. And nations such as the United States and Italy where government actions
were less aggressive, and occuring more slowly in stages state-by-state or
province-by-province, the disease has spread much more widely through the
population and will take considerably longer to contain.
When a disease like this originates or takes hold in a country, perhaps that
country should quickly limit its outbound travel of its citizens to prevent the
spread of disease outside to other nations. And within that country perhaps a
nationwide short-term stay at home and social distancing order (if you need to go
out to get necessities) should be placed, with widespread testing to quarantine
those infected and who have been in contact with those infected. For those that
would be concerned about future implementations of such policies because of the
economy or the social tolls of being isolated, remember these policies would only
need to be short-term because aggressive action early on allows for quicker
containment, so effects on the economy, etc., would be much shorter and less
severe than we are seeing now with COVID-19. And why take aggressive nationwide
action even if cases maybe only in certain regions? To make sure the disease does
not spread rapidly into the uninfected regions.
...WHAT AREAS I COLLECTED DATA FOR... 1) China, where COVID-19 originated 2) Italy, one of the first nations outside of China where the number of cases of COVID-19 increased rapidly 3) South Korea, where COVID-19 was quickly contained 4) Nations where I have family and friends, the United States, United Arab Emirates, and Egypt 5) Within the United States, the following states: a) Michigan and North Carolina where I have family and friends b) California, Washington, and New York which were the first states to have a rapid increase in the number of COVID-19 cases. c) West Virginia, the last state to report its first COVID-19 case ...SOURCES FOR DATA... 1) For global COVID-19 case count, https://www.worldometers.info/coronavirus/ 2) For a state by state breakdown in the United States, https://www.worldometers.info/coronavirus/country/us/ 3) For a county-level breakdown in Michigan, https://www.michigan.gov/coronavirus 4) For a county-level breakdown in North Carolina, https://www.newsobserver.com/news/local/article241168731.html 5) New York state total and New York City total, https://covid19tracker.health.ny.gov/views/NYS-COVID19-Tracker/NYSDOHCOVID- 19Tracker-TableView?%3Aembed=yes&%3Atoolbar=no ...CALCULATIONS TO NORMALIZE DATA.... Warning! Note that while the percentages described below have been of low numbers, it only has taken a very small percentage of the population getting infected to overwhelm the medical system, which forces hospitals to make priorities as to who will or will not get COVID-19 treatments as well as other treatments for other serious diseases such as cancer. For example Italy on 3/11/2020 with only 0.021% of the national population infected with COVID-19 had hospitals reaching capacity (https://www.theatlantic.com/ideas/archive/2020/03/who-gets-hospital- bed/607807/). Comparing nations or regions (state, county, city, province, etc.) simply by looking at the total number of cases may not provide a fair comparison as larger population areas naturally will have a higher number of cases. Therefore I converted the data by dividing by population to give a normalizing percentage, for instance: % infected, nation = (total cases nationwide/total population of nation)*100 % infected, state = (total cases statewide/total population of state)*100 % infected, county = (total cases countywide/total population of county)*100 % infected, city = (total cases citywide/total population of city)*100 I also did the a similar normalization for the number of new cases per day, in a metric I called "percent chance of getting infected." For example in a population of 100, if you are getting 1 new case each day that the percent chance of the remaining uninfected 99 getting infected is (1/99)*100 = 1.01%. On the next day when another 1 case happens the chance of the uninfected 98 getting infected becomes (1/98)*100 = 1.02%. Then lets say on the third day the population is now getting 3 new cases per day, so now 95 are uninfected and the chances go up to (3/95)*100 = 3.15%. As such I calculated: Number of Uninfected = Total Population - Total Number of Cases % chance of becoming infected = (New Cases Today/Number of Uninfected)*100 Although I think this metric is proportional to the likelihood of getting infected, the small resulting values should not be interpreted as literal or to minimize the seriousness of the situation, because this is based on the number of new confirmed cases, so if testing supplies in a region are not adequate or a large number of people with flu-like symptoms decide not to get themselves tested, then the true number of COVID-19 cases in reality is larger and this percentage could actually be higher than what has been calculated thus far. And if you venture to certain hospitals, streets, buildings, etc. where the virus is present or in high concentration, the chances of becoming infected go up and is likely higher than reflected by the calculation. Rather, I use this calculation to understand if the number of new cases per day is increasing, staying the same, or decreasing, and this metric is normalized by population to be able to compare one region with less population to another region with more population more fairly. The final metric is the mortality rate calculated as: mortality rate % = (Total Deaths/Total Number of Cases)*100 ...WORLDWIDE DATA...
Figure 1 - Percent of Population confirmed infected with COVID-19 in the nations I am currently tracking.
Figure 2 - Approximation of percent chance of becoming infected in the nations I am currently tracking. I made this approximation by taking the number of new cases per day and dividing by the uninfected population. See disclaimer within this figure about the limitations of this approximation, this metric should not be solely used in assessing your risk or decision making about COVID-19.
Figure 3 - Mortaility rate of COVID-19 cases in the nations I am currently tracking.
Reliable data for China is not available before January 16 (https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic_in_mainland_C hina), but the disease was underway as early as December 2019 when a mysterious severe flu-like illness was taking hold in the city of Wuhan in the Hubei province. Considering January 16 as the first day of reported cases, only eight days later on January 23 an aggressive lockdown was placed on the Hubei province which prevented regular travel in and out (https://en.wikipedia.org/wiki/2020_Hubei_lockdowns). Within Hubei, aggressive measures were taken such as monitoring how often individuals leave their residence for necessities, and authorities sent door-to-door to do fever measurements, taking people out of homes and into mandatory quarintine if a fever was found (https://time.com/5796425/china-coronavirus-lockdown/). Police presence outside of residential buildings forced many to order food and essentials online instead of venturing out (https://www.businessinsider.com/chinas-coronavirus- quarantines-other-countries-arent-ready-2020-3#coronavirus-testing-was-easily- accessible-and-free-1). According to Figure 2 below, 27 days after the start of the lockdown, and day 35 of the outbreak in China (February 20), COVID-19 cases in China were contained.
China's model does not appear to be the only solution as South Korea demonstrates. The first case in South Korea was logged on January 20, with an aggressive testing and quarantining strategy in place by Febuary 28, or 40 days into their outbreak (https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_South_Korea#cite_note -chosun-numberoftests-174). 12 days later, or day 52 of their outbreak on March 11, COVID-19 cases in South Korea were contained per Figure 2 below. South Korea demonstrates what happens when testing supplies are adequately available. Italy recorded its first confirmed case of COVID-19 on January 31, with a restriction of travel to and from China implemented immediately (https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Italy). However the travel ban alone was not effective, with a severe COVID-19 outbreak underway particularly in the northern provinces of Italy by March 11 (https://www.forbes.com/sites/davekeating/2020/03/12/italy-banned-flights-from- china-before-americait-didnt-work/#1c3827da481b). It is during this day (day 41 of their outbreak) that certain hospitals were reaching capacity, and according to Figures 1 below this was with only 0.021% of the national population infected with COVID-19, and according to Figure 2 only with 0.004% of the uninfected national population becoming infected per day (https://www.theatlantic.com/ideas/archive/2020/03/who-gets-hospital- bed/607807/). By day 50 of their outbreak, on March 20th, certain hospitals were overwhelmed, and this was with only 0.078% of the national population infected per Figure 1 and 0.010% of the uninfected national population becoming infected per day according to Figure 2 (https://www.businessinsider.com/video-tour- coronavirus-icu-ward-bergamo-italy-worst-apocalyptic-2020-3). Instead of a concerted nationwide response to the virus, Italy had a province- by-province approach, with the first restrictions going into effect on February 22 (day 23 of their outbreak) in the northern provinces, with those restrictions tightening on March 1 (day 31 of their outbreak) and again tightening further on March 7 (day 37 of their outbreak). It was not until day 50 of the outbreak (March 20) that restrictions covering the entire nation were issued (https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Italy). The multiple rounds of tightening restrictions in northern Italy created confusion among its citizens as to what was and was not allowed, for example a women's account in Milan who was out shopping in public on March 9 found at this link (https://www.nytimes.com/2020/03/09/world/europe/italy-lockdown- coronavirus.html). In addition the rate of COVID-19 testing in Italy was not as high as South Korea, by March 17 in South Korea testing covered 3,692 tests per million people while Italy had only tested 826 per million people (https://www.cnn.com/2020/03/16/opinions/south-korea-italy-coronavirus- survivability-sepkowitz/index.html). One Italian town, Vo, however had a model similar to South Korea's aggressive testing and quarinting strategy, resulting in a quick containment of the virus (https://www.theguardian.com/commentisfree/2020/mar/20/eradicated-coronavirus- mass-testing-covid-19-italy-vo). There may also be a cultural effect of not taking the situation seriously or concerns about infringement on freedoms from government restrictions, for example a widespread defiance of orders as of March 24th mentioned at this link (https://qz.com/1824240/thousands-are-paying-fines- for-defying-italys-coronavirus-lockdown/). In summary, the disorganized slow (instead of an organized quick) shutdown of the nation, combined with inadequate testing in a government and culture that is based on having freedoms and individual skepticism (some not taking the COVID-19 virus seriously) appears to be why COVID-19 has hit Italy hard. But in these dynamics eventually the government issues necessary orders nationwide as the situation becomes severe, and in the public the seriousness of the situation eventually takes hold and a majority of the nation cooperates with government orders to stay at home and socially distance if in public, and for Italy this appears to have happened by late March. The result is, according to Figure 2, Italy has begun to see a slow down in COVID-19 infections, but unfortunately after the disease has infiltrated a large swath of the population, so now it will take additional weeks for Italy to have the disease contained. South Korea is also a free society, so it appears that a large amount of testing is needed in such a society and rather quickly to contain a disease like this (https://www.forbes.com/sites/carlieporterfield/2020/03/13/south-korea-sees- coronavirus-slowdown-without-a-lockdown-but-with-nearly-250000- tests/#4158e31d576b). Without adequate testing in a free society, it appears that a swift and strict government lockdown that temporarily impinges on freedoms appears necessary as China did. But if we pick the middle road like Italy, where government shutdowns are slow to rollout and society does not initially cooperate due to concerns about freedoms/skepticism about how serious the virus is AND there is not enough testing/quarantining to protect such a free society during a pandemic, the situation becomes severe. The United States is a large-scale version of Italy in these regards. The first confirmed case, in Washington state, was reported on January 21. Eleven days later on the 31st of January restrictions on travel to and from China were implemented (https://www.npr.org/sections/health- shots/2020/01/31/801686524/trump-declares-coronavirus-a-public-health-emergency- and-restricts-travel-from-c). Fifty-two days after the first confirmed case, on March 12, restrictions on travel to and from most European nations were placed (https://www.bbc.com/news/world-us-canada-51846923). Finally on March 19, 59 days since the first confirmed case, the State Department restricted all international travel of its citizens (https://thepointsguy.com/news/us-state-department-level-4/). However like Italy, the implementation of travel restricions, some of which were deployed early in the outbreak, were by themselves not effective in stopping the spread of the virus from within the United States. Like Italy, stay at home orders in the United States were not issued in a concerted manner at the national level, with the federal government leaving it to the State Governors to do so. The result is it took till April 9 (80 days since the first confirmed case) for most states to be under stay at home orders (https://www.wsj.com/articles/a-state-by-state-guide-to-coronavirus-lockdowns- 11584749351). And even by this date, the Dakotas, Nebraska, Arkansas, and South Carolina still had no stay at home orders. Furthermore most state restrictions do not restrict travel across counties within the state, or state-to-state travel such that a state or locale with a high number of cases has potential to spread the disease elsewhere within the United States. Some exception to this is in Maine, Vermont, Ohio, West Virginia, Kansas, Montana, New Mexico, Alaska, and where travelers from out-of-state have to self quarantine for a period of time after arriving into the state. But the South Korean model shows that a free society without stay at home orders can contain the disease with massive amounts of testing and quarantining. However the United States has been behind in stocking up enough testing supplies, with the shortage creating challenges in getting tested (if you wanted to get tested) through March when the disease spread began to ramp up (https://www.refinery29.com/en-us/2020/03/9582955/where-coronavirus-test- shortage-usa). Even by April 10 the increased testing capacity in the United States did not reach the coverage per person that Italy and South Korea had reached by that date (https://www.vox.com/2020/4/10/21214218/trump-coronavirus- testing-social-distancing). And also like Italy, in the United States their appears to be a cultural effect of not taking the situation seriously or concerns about infringement on freedoms from government restrictions, for example the following stories of defying stay at home and public social distancing orders at the following links: https://patch.com/maryland/annapolis/almost-700-marylanders-warned-violating- stay-home-order https://www.tampabay.com/news/health/2020/04/02/coronavirus-in-florida-latest- stay-at-home-order-state-death-toll-passes-100-defiant-pastor-cancels-service/ https://www.nbcsandiego.com/news/local/deputies-issuing-citations-to-people- found-violating-stay-at-home-order-at-beaches/2299597/ https://www.reuters.com/article/us-health-coronavirus-usa-church/louisiana- church-holds-services-defying-coronavirus-stay-at-home-order-idUSKBN21N0UU https://wgntv.com/news/coronavirus/wife-of-illinois-mayor-cited-for-violating- stay-at-home-order-after-attending-bar-party-police-say/ It now appears the United States has caught up to the seriousness of the situation like Italy eventually did, with a majority of the states now under a stay at home order and the public appearing to take social distancing more seriously. But because the response was lagging like Italy, according to Figures 1 and 2 the United States appears to be on Italy's trajectory, with Italy about 14 days ahead of the United States. I tested this hypothesis on April 2nd by predicting the number of nationwide confirmed COVID-19 cases in the United States over 14 days by using Italy's data in Figure 2. So far this forecasting has proven fairly accurate, see United States experimental forecast section below for more details. Therefore this means like Italy the United States has seen the virus infiltrate a large swath of the population during the slow response ramp up, therefore it will take additional weeks of current practices to have the disease contained.
...UNITED STATES DATA BROKEN DOWN BY STATES...
Figure 4 - Percent of Population confirmed infected with COVID-19 in states I am currently tracking.
Figure 5 - Approximation of percent chance of becoming infected in states I am currently tracking. I made this approximation by taking the number of new cases per day and dividing by the uninfected population. See disclaimer within this figure about the limitations of this approximation, this metric should not be solely used in assessing your risk or decision making about COVID-19.
Figure 6 - Mortality rate of COVID-19 cases in states I am currently tracking.
West Virginia became the last state in the United States to report its first confirmed case of COVID-19 on March 18. In part this appeared to be from a shortage of testing supplies within the state as demonstrated by the couple who comprised the first two cases in the state who had to travel out of state to find a COVID-19 test in a timely manner (https://www.cnn.com/2020/03/21/us/west- virginia-coronavirus-patient-one-test/index.html). But the story of a shortage of test supplies is common in all states in the United States, so additional factors that may have played a role in West Virginia's belated arrival of COVID-19 are the lack of international travelers through the state and also largely rural and mountainous setting of the state that naturally keeps people in reduced contact from each other (https://www.nytimes.com/2020/03/14/us/weve-got-a-monster-thats- looming-west-virginia-is-the-last-state-without-a-coronavirus-case.html). It appears for West Virginia, the state government took more proactive action than other states, already closing schools on March 13 and bars, casinos, and restaurants on March 17 before the state's first confirmed case. And only on the sixth day after the state's first confirmed case, on March 23, a stay-at-home order was issued (https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_West_Virginia). The stay-at-home order unlike many states requires travelers from out-of-state to self-quarintine upon arrival (https://www.wsj.com/articles/a-state-by-state- guide-to-coronavirus-lockdowns-11584749351). Per Figures 4 and 5, it appears the proactive orders and the state's rural/mountainous setting that naturally keeps people further away from each other has allowed West Virginia to be one of the leading states in keeping coronavirus cases reduced. North Carolina's governor began a stay at home order on March 24, twenty-four days after the state's first confirmed COVID-19 case and with 880 total cases statewide at the time (https://wlos.com/news/local/north-carolina-has-another- death-as-more-local-orders-begin). Initially California had a county-by-county response before issuing a statewide stay at home order. Santa Clara County (San Fransisco area) banned gatherings exceeding 1000 people on March 9, 45 days after the state's first confirmed case and when only 133 cases were confirmed statewide. A statewide order for banning gatherings exceeding 250 people was issued on March 12, 48 day after the first confirmed case with 198 cases now confirmed statewide. San Fransisco area counties had issued stay at home orders on March 16, 52 days after the first confirmed case with 472 cases confirmed statewide. Additional counties on the west coast of California issued stay at home orders the next day on March 17. California's statewide stay at home order came on March 19, 55 days after the state's first confirmed case and with 1057 cases confirmed statewide (https://en.wikipedia.org/wiki/U.S._state_and_local_government_response_to_the_20 20_coronavirus_pandemic#California). Washington state had a series of orders issued by the governor's office that eventually ramped up to a statewide stay at home order. On March 11, 51 days after the state's confirmed case and with 375 confirmed cases statewide, gatherings exceeding 250 people were banned in King, Snohomish, and Pierce Counties. The next day on March 12, schools were ordered closed in these counties. On March 13 school closures were expanded to a statewide level. On March 15, 55 days after the state's first confirmed case and with 750 confirmed cases statewide, sit-down restaurants were ordered closed and gatherings exceeding 50 people were banned statewide. It was not until March 23 that finally a blanket statewide stay at home order was issued, 63 days after the state's first confirmed case and with 2221 cases confirmed statewide (https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Washington_(state) #Government_response). Michigan's statewide stay-at-home order also came on March 23rd, although this was only the 13th day after the state's first confirmed case the number of confirmed cases had risen rapidly to 1328 by that date (https://www.michigan.gov/whitmer/0,9309,7-387-90499_90705-522626--,00.html). New York's statewide stay-at-home order came on March 20th, twenty days after the state's confirmed case but with already 8403 confirmed cases statewide (https://nypost.com/2020/03/20/coronavirus-in-ny-cuomo-orders-lockdown-shuts- down-non-essential-businesses/). The aforementioned government actions are summarized in Table 1 below. It is quiet striking that the current penetration of COVID-19 in individual states appears to be quiet proportional to how many cases were present statewide at the time each state's of the stay at home order issuance, with more penetration if more cases were present at the time of the order issuance (for example New York having 8,403 cases during the stay at home order issuance and now having 1% of its population infected), and less penetration if few cases were present at the time of order issuance (for example West Virginia only having 20 cases during the stay at home order issuance and now having only 0.035% of its population infected). States like Michigan and New York, where the cases built up rapidly only days after the state's first confirmed case, perhaps were at a disadvantage of not having much time to issue a stay at home order before the cases spread rapidly in metropolitan areas where several people are in close contact (for New York state, the New York City area, and for Michigan the Detroit area). The data in Table 1 seems to suggest that timing is very sensitive, in regards to the number of confirmed cases, in the issuance of stay at home orders, especially in the United States where testing supplies were limited to know who exactly had COVID-19. Because if a large number of people are already infected by the time the order is issued, the severity of the outbreak becomes worse.
Table 1 - Timing of Statewide Stay-At-Home Order Versus Penetration of COVID-19 Outbreak
A similar trend was noted in this article which explains why Michigan has more coronavirus cases than its neighboring states which had fewer cases at the time their stay at home orders were issued (https://www.bridgemi.com/michigan-health- watch/why-did-coronavirus-spread-so-fast-michigan-compared-neighbors). Some good news though is seen in Figure 5 where the number of new cases per day has notably decreased in the hard-hit states of Michigan, New York, and Washington over the last few days, attributable to the stay at home orders. If current trends continue, then a reduction in new cases per day also seems to be manifesting in West Virginia as well. California and North Carolina have kept the number of new cases per day flattened at a plateau (again attributable to the stay at home orders), but have not seen a notable decrease from the plateau yet.
...SOUTHEAST MICHIGAN DATA BROKEN DOWN BY COUNTIES...
Figure 7 - Percent of Population confirmed infected with COVID-19 in counties in southeast Michigan I am currently tracking.
Figure 8 - Approximation of percent chance of becoming infected in counties in southeast Michigan I am currently tracking. I made this approximation by taking the number of new cases per day and dividing by the uninfected population. See disclaimer within this figure about the limitations of this approximation, this metric should not be solely used in assessing your risk or decision making about COVID-19.
Figure 9 - Mortality rate of COVID-19 cases in counties in southeast Michigan I
am currently tracking
Wayne County is home to the Detroit and Dearborn metropolitan areas. Oakland
County is home to the Pontiac metropolitan area. Macomb County has the
metropolitan areas of Troy and Warren. Livingston County has more of a rural
setting. While Washtenaw and Ingham counties are homes to Ann Arbor and Lansing,
those counties have a lower population as well because of the more rural
landscape immediately surrounding those cities. The trends in Figures 7 and 8
suggest the higher population counties of Wayne, Oakland, and Macomb have had a
more rapid spread of COVID-19 likely due to the closer proximity of people to
each other. However Figure 8 suggests good news in recent days as these counties
have seen a slow down in the number of new cases per day.
...NORTH CAROLINA DATA BROKEN DOWN BY COUNTIES...
Figure 10 - Percent of Population confirmed infected with COVID-19 in counties in North Carolina I am currently tracking.
Figure 11 - Approximation of percent chance of becoming infected in counties in North Carolina I am currently tracking. I made this approximation by taking the number of new cases per day and dividing by the uninfected population. See disclaimer within this figure about the limitations of this approximation, this metric should not be solely used in assessing your risk or decision making about COVID-19.
Figure 12 - Mortality rate of COVID-19 cases in counties in North Carolina I am currently tracking
Wake and Durham counties are more densely populated counties in the Raleigh- Durham region of the state, but it appears based on Figures 10 and 11 COVID-19 has hit Durham County harder than Wake County. Chatham and Johnston counties are more rural counties surrounding the Raleigh-Durham area, and it is interesting to note in Figures 10 and 11 these counties have seen a surge in cases in recent days, especially in Chatham county. This rural spread is a bit surprising given that people are not as close in contact. Mecklenburg County is home to North Carolina's most populous city, Charlotte. Thus with many people close in contact with each other so it is not as surprising to see it as a harder hit area in the state per Figures 10 and 11. Cumberland County is home to Fayetteville, and Guilford and Forsyth Counites are home to the triad area (Winston-Salem and Greensboro). These counties have not been hit as hard, but these counties have a lower population per county, so perhaps people are not as close in contact as the Raleigh-Durham and Charlotte areas.
...NEW YORK DATA...
Figure 13 - Percent of Population confirmed infected with COVID-19 in New York
City (NYC) and New York State as a whole including NYC
Figure 14 - Approximation of percent chance of becoming infected in New York City (NYC) and New York State as a whole including NYC. I made this approximation by taking the number of new cases per day and dividing by the uninfected population. See disclaimer within this figure about the limitations of this approximation, this metric should not be solely used in assessing your risk or decision making about COVID-19.
New York state has quickly and by far led the United States in the number of COVID-19 cases per state. Even when normalizing by population, New York has the highest percent population infected compared to the remaining states. A big catalyst for this appears to be New York City itself where on any given day the city alone has accounted for half of the state's total number of cases. Given the very high population density of the city where millions are naturally in close contact with each other, the rapid spread of the virus here makes sense. However Figure 14 suggests good news in recent days as the city and state as a whole are seeing a decrease in the new number of cases per day.
...UNITED STATES EXPERIMENTAL FORECAST CREATED APRIL 2...
Figure 15 - Forecast paradigm used on April 2. Since the United States parallel Italy in the dynamics leading to a large outbreak (behind in testing, slow roll-out of government restrictions), and graphically mimiced Italy in this chart except being 14 days behind, I assumed the next 14 days of United States COVID-19 activity would look like Italy (the blue-dashed section of the USA curve was my forecast).
On April 2nd, I created an experimental forecast for the United States predicting the total number of COVID-19 cases and fatalities. The paradigm for this forecast is explained in the caption of Figure 15, where I essentially mimiced Italy for the next 14 days since the events prior to April 2 were much like Italy. The forecast curve in Figure 15 was put in the column "% chance of getting infected" in Table 2. From there, the following metrics could be calculated in the forecast for each day: New Cases in One Day = (% Chance of Getting Infected/100)*(Total Uninfected Previous Day) Total Cases = Total Cases Previous Day + New Cases in One Day Total Uninfected = USA Population - Total Cases % Population Infected = (Total Cases/USA Population)*100 The mortality rate forecast in Table 2 was based on the observation of the mortality rate increasing by 0.1% each day from April 1 to April 2 and extrapolating that forward. The forecast mortality rate was then used to forecast the total number of COVID-19 fatalities as follows: Total Deaths = (Mortality Rate %/100)*Total Cases Based on the information in Table 2 as well as Figures 16 and 17 this forecasting strategy seems to verify fairly accurately. The largest error is with the forecast fatalities which seems to be increasing faster than forecast unfortunately. I plan to release an updated forecast that reflects the latest developments in the coming days. Remember, these verifying forecasts reflect a slowdown in the number of new cases per day nationally, so continue to stay at home as much as possible, socially distance if you have to be in public, and practice good hygiene habits including with the cleaning of packaging of bought items picked up or delivered to your home.
Table 2 - Tabular view of USA experimental forecast for COVID-19 cases issued April 2
Figure 16 - Graphical view of USA experimental forecast for COVID-19 cases issued
April 2, total number of cases
Figure 17 - Graphical view of USA experimental forecast for COVID-19 cases issued April 2, total fatalities