Back

A Solution to the Kermack and McKendrick Integro-Differential Equations

Duclos, T. G.; Reichert, T. A.

2022-04-29 epidemiology
10.1101/2022.04.28.22274442 medRxiv
Show abstract

In this manuscript, we derive a closed form solution to the full Kermack and McKendrick integro-differential equations (Kermack and McKendrick 1927) which we call the KMES. We demonstrate the veracity of the KMES using independent data from the Covid 19 pandemic and derive many previously unknown and useful analytical expressions for characterizing and managing an epidemic. These include expressions for the viral load, the final size, the effective reproduction number, and the time to the peak in infections. The KMES can also be cast in the form of a step function response to the input of new infections; and that response is the time series of total infections. Since the publication of Kermack and McKendricks seminal paper (1927), thousands of authors have utilized the Susceptible, Infected, and Recovered (SIR) approximations; expressions putatively derived from the integro-differential equations to model epidemic dynamics. Implicit in the use of the SIR approximation are the beliefs that there is no closed form solution to the more complex integro-differential equations, that the approximation adequately reproduces the dynamics of the integro-differential equations, and that herd immunity always exists. However, the KMES demonstrates that the SIR models are not adequate representations of the integro-differential equations, and herd immunity is not guaranteed. We suggest that the KMES obsoletes the need for the SIR approximations; and provides a new level of understanding of epidemic dynamics.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bulletin of Mathematical Biology
84 papers in training set
Top 0.1%
33.9%
2
Infectious Disease Modelling
50 papers in training set
Top 0.2%
6.5%
3
Journal of Mathematical Biology
37 papers in training set
Top 0.1%
6.5%
4
Mathematical Biosciences
42 papers in training set
Top 0.1%
5.0%
50% of probability mass above
5
Journal of Theoretical Biology
144 papers in training set
Top 0.3%
3.7%
6
Royal Society Open Science
193 papers in training set
Top 0.8%
3.2%
7
Scientific Reports
3102 papers in training set
Top 40%
3.2%
8
Physical Review E
95 papers in training set
Top 0.4%
2.8%
9
PLOS ONE
4510 papers in training set
Top 44%
2.7%
10
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 27%
2.1%
11
Mathematical Biosciences and Engineering
23 papers in training set
Top 0.3%
1.8%
12
Physical Review Research
46 papers in training set
Top 0.4%
1.5%
13
Epidemics
104 papers in training set
Top 1%
1.5%
14
Journal of The Royal Society Interface
189 papers in training set
Top 3%
1.5%
15
PLOS Computational Biology
1633 papers in training set
Top 17%
1.5%
16
International Journal of Infectious Diseases
126 papers in training set
Top 2%
1.4%
17
Physical Biology
43 papers in training set
Top 1%
1.3%
18
Frontiers in Physics
20 papers in training set
Top 0.6%
1.0%
19
Chaos: An Interdisciplinary Journal of Nonlinear Science
16 papers in training set
Top 0.2%
0.9%
20
PNAS Nexus
147 papers in training set
Top 1.0%
0.9%
21
Mathematics
11 papers in training set
Top 0.4%
0.8%
22
Emerging Infectious Diseases
103 papers in training set
Top 3%
0.8%
23
Chaos, Solitons & Fractals
32 papers in training set
Top 2%
0.7%
24
Biomechanics and Modeling in Mechanobiology
25 papers in training set
Top 1%
0.5%
25
The European Physical Journal Plus
13 papers in training set
Top 0.9%
0.5%
26
Nonlinear Dynamics
10 papers in training set
Top 0.5%
0.5%
27
The Journal of Chemical Physics
49 papers in training set
Top 0.6%
0.5%
28
eLife
5422 papers in training set
Top 63%
0.5%