The influence of gender on COVID-19 infections and mortality in Germany: Insights from age- and gender-specific modeling of contact rates, infections, and deaths in the early phase of the pandemic

Abstract

Recent research points towards age- and gender-specific transmission of COVID-19 infections and their outcomes. The effect of gender, however, has been overlooked in past modelling approaches of COVID-19 infections. The aim of our study is to explore how gender-specific contact behavior affects gender-specific COVID-19 infections and deaths. We consider a compartment model to establish short-term forecasts of the COVID-19 epidemic over a time period of 75 days. Compartments are subdivided into different age groups and genders, and estimated contact patterns, based on previous studies, are incorporated to account for age- and gender-specific social behaviour. The model is fitted to real data and used for assessing the effect of hypothetical contact scenarios all starting at a daily level of 10 new infections per million population. On day 75 after the end of the lockdown, infection rates are highest among the young and working-age, but they also have increased among the old. Sex ratios reveal higher infection risks among women than men at working ages; the opposite holds true at old age. Death rates in all age groups are twice as high for men as for women. Small changes in contact rates at working and young ages have a considerable effect on infections and mortality at old age, with elderly men being always at higher risk of infection and mortality. Our results underline the high importance of the non-pharmaceutical mitigation measures (NPMM) in low-infection phases of the pandemic to prevent that an increase in contact rates leads to higher mortality among the elderly, even if easing measures take place among the young. At young and middle ages, women's contribution to increasing infections is higher due to their higher number of contacts. Gender differences in contact rates may be one pathway that contributes to the spread of the disease and results in gender-specific infection rates and their mortality outcome. To further explore possible pathways, more data on contact behavior and COVID-19 transmission is needed, which includes gender- and socio-demographic information.

Fig 1. Sex ratios of COVID-19 incidence.

Sex ratio (men/women) of COVID-19 incidence through January 5, 2021 by age (data source: Robert Koch Institute Dashboard, authors’ calculations).

Fig 2. Gender-specific contact rates.

Ratio of the average number of contacts among men compared to women, Data Source: [15].

Fig 3. SEIRD compartment model for COVID-19.

SEIRD compartment model with 5 transitions. (S → E: susceptible person becomes exposed to the virus, E → I: exposed person becomes infectious, E → R: exposed person is removed due to recovery, I → R: infectious person is removed due to recovery, I → D: infectious person is removed due to death).

Fig 4. Differences in contact rates by sex.

Sex ratios (men/women) of overall contact rates λ_ab in Germany for different sex and age groups in the absence of lockdown measures (based on [15]).

Fig 5. Diagram of the scenario analysis approach.

Outline of the scenario analysis. For every compartment C, C_a(t) denotes the number of people from group a which are in compartment C at time t; I_a,cum denotes cumulative number of infections. S_a(t) on the base reference date are obtained from Destatis (Federal Statistical Office of Germany); I_a(t), R_a(t) and D_a(t) on the base reference date are obtained from the Robert Koch Institute Dashboard.

Fig 6. Simulation results for Scenario 1.

Number of active infections and gender ratio (men/women) in Scenario 1 (intervals represent 80% range due to parameter uncertainty).

Fig 7. Simulation results for Scenario 2.

Number of active infections and gender ratio (men/women) in Scenario 2 (intervals represent 80% range due to parameter uncertainty).

Fig 8. Simulation results for Scenario 3.

Number of active infections and gender ratio (men/women) in Scenario 3 (intervals represent 80% range due to parameter uncertainty).

Fig 9. Simulation results for Scenario 4.

Number of active infections and gender ratio (men/women) in Scenario 4 (intervals represent 80% range due to parameter uncertainty).