Disease Modelling Time span (DiMoT) is a set as a domain of functions for which the development of the disease is mathematically described. Functions describe specific properties.
Introductory Example[edit | edit source]
As an introductory example, we assume that the disease modelling period is 14 days. In this disease modeling period, a function should be created to represent the infectivity over this 14-day period. The function should be 100 if 100% of the infected patients are themselves infectious at this time.
In this introductory example the percentage of infected persons are represented by 3 different functions with different developments in time. The curves are fictitious and are only intended to illustrate the principle of disease modelling time spans.
- The red curve shows a temporal development in which people cannot infect others for the first 7 days and after that 7 days 100% of are able to infecte other susceptible citizens for the next 7 days. The disease modelling time span is 14 days.
- in the blue curve 100% of all infected citizens are infectious on the 3 day after infection, after the third day less and less infected persons become infectious for others. This is another function describing a different temporal development with the same DiMoT of 14 days.
- in the green curve the percentage of infected persons increases to 100% by the 11th day and then 100% infectious for 3 days and then the percentage of infected patients decreases again.
Definition: DiMoT[edit | edit source]
A Disease Modelling Time span (DiMoT) can defined as
- continuous DiMoT by an Intervall
- discrete DiMoT as a finite set of real values with .
Example[edit | edit source]
A Disease Modelling Time span (DiMoT) for the example above is in
- continuous DiMoT case the intervall or as
- discrete DiMoT the finite set of real values with .
Learning Tasks[edit | edit source]
- Hospitalisation and mortality show a delay in the increase and decrease of numbers. Explore the concept of Latency and explain how the latency can be visualized with graphs. What is the difference and what are the similarities between just a latency of a signal and a dependency and delay of hospitalization and mortality. If a signal delay can be identified, explain how preparedness for response can take that into account if latency provides sufficient response time for Risk Management.
Probability Density and Report Time of Cases[edit | edit source]
Now we use the DiMoT as the domain of a probability density over an interval an extend the DiMoT interval to the negative numbers. This example shows a probabilty density for the reported time stamp of cases and the (estimated?) time stamp of the infection of the patient. The reporting time can be regarded as a date that is documented in the database e.g. of a public health agency. The point in time when a patient was infected can be a very fuzzy information with assumption of events that might expose the patient to other COVID-19 positive citizens (which might not know that they were infectious at that time). Neverthess some information can be gathered to identify the time of infection and the appearance of first symptoms and the time and date when the test was performed. Assume we create a probabilty density on the negative numbers that show number of reported case and the delay from the time of infection and the time of reporting the positive case. We start with the absolute numbers.
- Assume a reported case at day 0 was infected 13 days before and we define a discrete probabilty distribution on the negative integer numbers, then we assign a +1 to -13.
- if we have 10 reported cases that are (probably) infected 13 days before their case was reportedm then we assign a +10 to -13.
- we apply that for other times spans between reporting time and infection time.
- if the infection time can be roughly determined between 11th,12th or 13th day before the reporting time, then we assign to -11,-12 and -13 for a specific case.
- the following curves show different examples for values - these values a not real values. The should illustrate the basic principles for functions on the DiMoT set as domain.
Graph with absolute Number of Cases[edit | edit source]
The following graph are demo numbers to show the process from creating a discrete probability distribution from a given number of cases. Assume for this chart we could ask COVID-19 positive patients at which event they might are infected.
From Graph for the Number of Cases towards a Probability Density[edit | edit source]
We sum up all cases for data collection 1, 2 and 3 (3 graph with the cases above). We devide each absolute number by sum in the respective column and calculate the Empirical Probability. The following chart is generated with CSV2Chart from the CSV file in the respository on GitHub.
Discrete probabilty distribution can be calculated on the DiMoT by deviding the absolute number or
See also[edit | edit source]
References[edit | edit source]
- "Wikiversity Files". niebert.github.io. Retrieved 2020-10-08.