Although every input matters in a model, the most important and perhaps most commonly discussed in the media is the R 0 input, which is the assumption of the number of people that any individual will infect while they have the disease before they either die or recover. This should not be confused with the “R” in the model’s name, which is completely different. In broad conceptual terms, each time someone in the population who is still susceptible contracts the disease, they are assumed to pass it on to R 0 others of those who remain in the susceptible population. Eventually, if enough people have contracted the disease in the given population, there is nobody left who is susceptible, meaning the last people to get it can no longer pass it on. At this point the population has achieved the much-discussed herd immunity. Prior to that point, development of a vaccine can move the population to the same result if it can confer sufficient immunity to enough vaccinated people. In the absence of a vaccine, mitigating efforts such as those that have been employed in the U.S. and around the world can reduce the R 0 and slow the rate of spread. Since both the R 0 and the recovery rates are time sensitive, mitigation can slow the infection rate to less than one, resulting in a decline in total cases over time even though neither herd immunity nor a vaccine have been achieved. However, the reduction is like the proverbial grasshopper hopping halfway to the door each time. It gets closer and closer but never fully makes it. As is evident from the description, the model, like any model depends upon a number of key assumptions, not only for the inputs used, but also for the mathematical expression of the interaction between the inputs. Assumptions for the infectiousness of the disease, the length of latency (infected but asymptomatic), the length of actual symptoms, and the death rate are all required to run the model. For more detailed or comparative versions and iterations, assumptions must be made or varied for the percentage requiring hospitalization, the percentage requiring ICU or ventilators, the reduction in R 0 due to mitigation efforts (closures, social distancing, masks, etc.), the effectiveness of treatments in reducing mortality and infection length, the effectiveness of testing and contact tracing, and many other alternatives that may be considered to study various scenarios. The assumptions built into the model regarding the interactions between inputs are necessarily based, at least initially, on the behavior of other, presumably similar, outbreaks. The assumptions for the inputs must be initially based upon the same data, only being adjusted over time as more current and disease specific data becomes available. Eventually even the model’s internal interactions may be adjusted to reflect current and disease specific observations, but this takes longer. As adjustment are made when new data become available, the models will inevitably yield different results.
∴ PROGNOSIS
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