Quantitative analysis on the effectiveness ofcontact-tracing apps
Asako Chiba
Tokyo Foundation for Policy Research1

Contents• Overview• Model and Data
• Analysis on the Effects of Contact-tracing Apps
• Summary• (Appendix) Lockdown2

Overview3Contact-tracing apps as policy tools• Lockdown generates a large amount of economic costs.
• High hopes for the combination of testing and apps.
• Substantial reduction of cases when the apps are downloaded by
56% of the population. (Fraser et al., 2020)• Several fundamental questions unanswered:• Why apps work?
• In what condition apps work?
• Are the apps more effective than the other policies?
• Trade-off between the epidemic suppression and quarantine.
4Quantitative analysis most in need
• A model should be detailed such that• it includes multi-dimensional heterogeneity in population.
• it characterizes coronavirus.
• it serves as a platform to compare various policies.
• Limitation in analytical models (SIR)• What the present research does:
A comprehensive study on contact-tracing apps,
using an agent-based model (ABM) and individual census data5Summary
1. Fraser-type apps (the alerted contacts get quarantined)
excludes the virus outbreak if sufficiently large number ofpeople download them.
2. Such strong prevention of virus outbreak cannot be achieved
by Japan-type apps (the alerted contacts get tested).
3. The effectiveness of Fraser-type apps lies in the quarantine of
the noninfectious contacts, who turn infectious later.
4. Extremely high download rate of Fraser-type apps not only
excludes the virus but also reduces the number of thequarantined.
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Features of the analysis• A hypothetical society constructed by individual census data.
• Detailed modeling of coronavirus and human networks.
• Intuition on mechanisms and conditions where apps work.
• Configuration of the apps
• Download rate
• Probability of testing the symptomatic• Trade-off between the epidemic suppression and quarantine.

7Model and Data

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Model outline
Kerr et al. (2020)• An uninfected person becomes infected probabilistically if she meets withan infected person.
• An infected person gets worse or recover probabilistically.
Complication of the model to reflect the real world and to introduce policies• Attribution of individuals (age, sex, prefecture, job, and industry).
• Places where people meet with each other（home, workplace, school,community, customer service, and nursing home）.
• Super-spreader(infection is more likely in 20% of all environment).
• Isolation of severe and critical patients due to the inability to go out.
• Contact-tracing apps that search for the contacts in the last 7 days.
9Definition of each status

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Expansion of virus outbreak• The probability with which an uninfected person B catch the virus
from an infected person A• Likelihood of transmission where A and B meet (detailed in thenext section)• A’s relative transmissibility• B’s relative susceptibility• Transmissibility in the early stage is twice as high as that in the later.
• Susceptibility of the elderly is higher than that of the young.
• Super-spreader: Likelihood of transmission in randomly selected 20%of all contacts is 50 times as high as that in the rest.
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An artificial small society
Randomly select 25 thousand people from 1.25 million in individual
census data in Japan, and create each selected person’s family. To
maintain the ratio among the age groups, single households of age over
60 are duplicated.
• Area: Japan• Population：75,614• Attribution：Age, sex, prefecture, job, industry and size of her
workplace13

Networks in each place•

Severe and critical patients are quarantined. (Their contacts at homes and nursing homes decrease by 20%,those in their communities decrease by 90%, and those other places cease.)

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Simulations• Each result shows the value averaged over simulations of 100times.
• Period: 500 days starting from February 14th .
• Every policy is introduced on the 33rd day of the simulation.
• Evaluation of each scenario for practical use: whether the virus isexcluded (less than around 5 incidents at its peak).

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The effects of contact-tracing apps

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How are contact-tracing apps effective?
• Fraser et al.(2020) concludes that the apps prevent the outbreakof pandemics, assuming that the contacts of the symptomatic
becomes isolated upon getting the alert.
• In Japan, the most common app recommends the contacts of theconfirmed positive to get tested.
• The analysis clarifies the difference of the effects these twodifferent apps bring. (Fraser-type vs. Japan-type)• Target age: All of the people of age between 15 and 70 (65.8%of all population) have smartphones, whereas the rest of them donot.
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Scenario with Japan-type apps
On the 33rd day, X% of the people of the target age download the app.
Daily PCR tests are conducted on a certain fraction of the symptomatic.
It takes a day that the tested symptomatic receive the results.
The confirmed positive are hospitalized＊ (All of her contact networks
become inactive) and register the test results if she has the app.
5. The alerted contacts get tested.
6. It takes a day that the tested contacts receive the results. The confirmed
positive are hospitalized and register the test results.
1.
2.
3.
4.

＊Hospitalized until recovery.
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Scenario with Fraser-type apps
1. On the 33rd day, X% of the people of age between 15 and 70 download
the app.
2. From then on, daily PCR tests are conducted on a certain fraction of thesymptomatic.
3. It takes a day that the tested symptomatic receive the results.
4. The confirmed positive become hospitalized＊ (All of her contact
networks become inactive) and register the test results if she has the app.
5. The alerted contacts are isolated (For 14 days, their contact networks inworkplaces, schools, communities and services decrease to 10% of thosein normal times).
＊Hospitalized until recovery.
19Only Fraser-type shows significant effects.
Japan-type apps

Fraser-type apps

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Three possible factors that create theinferiority of Japan-type to Fraser-type① False-negative effectSensitivity of 70% means that 30% of symptomatic people slip throughthe tests and possibly scatter the virus② Noninfectious effect
The noninfectious, who have caught the virus recently and have not
become infectious yet, cannot be detected.

③ Lockdown effect
For the uninfected alerted app-users, quarantine has the similar effects
as lockdown.
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Scenarios to identify the main factor:
Why do Fraser-type apps work?

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Noninfectious effect dominates.

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Benefits and costs of Fraser-type apps
• An efficient policy would reduce the number of the infected at a small
number of quarantine.
• Evaluate the efficiency of Fraser-type apps by measuring the duration of
total inactive time caused by quarantine and the one caused by illness.
Benefit: The inactive time due to hospitalization (The smaller, the better)
Cumulative number of the infected × Their expected duration ofhospitalization
Cost: The inactive time due to quarantine（The smaller, the better）
Cumulative number of the quarantined × Their expected duration ofquarantine (14 days)
24Extremely high download rate gains efficiency.

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U-curve in the efficiency plots
Case 1: Download rate is extremely low.
• Virus outbreak spreads.
• Only a small number of people get the alert and quarantined.
Case 2: Download rate is extremely high.
• Spread of the virus outbreak is prevented, since the contacts of the confirmed
positive are thoroughly quarantined.
• Thus, the number of quarantine is kept small. (Positive feedback)Case 3: Download rate is intermediate (Neither of the case 1 and 2).
• Limited effects on the spread of the virus.
• Not a small number of the contacts of the infected get the alert and quarantined.
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Sharp decrease in the number of quarantined
under high download rate.

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Summary
1. Fraser-type apps (the alerted contacts get quarantined)
excludes the virus outbreak if sufficiently large number ofpeople download them.
2. Such strong prevention of virus outbreak cannot be achieved
by Japan-type apps (the alerted contacts get tested).
3. The effectiveness of Fraser-type apps lies in the quarantine of
the noninfectious contacts, who turn infectious later.
4. Extremely high download rate of Fraser-type apps not only
excludes the virus but also reduces the number of thequarantined, which improves the efficiency.
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The next step

• Other countermeasures• Characteristics of coronavirus in more detail• Introduction of economic parameters

29Appendix
Effects of lockdown

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70%-lockdown have a significant effect.

31Vertical lockdown exclusive on the elderly is ineffective.

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Severe estimation under homogeneity.

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