2-stratum SEIRS model - Asia (D614) (2-strains Beta v9.0 10/17/2020)

Select location = , Initial + following strategy =

Spread day = Day of dominant spread (Holo), Infected on spread day (implicit) =
Beginning 1st phase = (Holo)
1st phase duration: days (ends on Holo)
1st isolation to vulnerable = (Vulnerable are all >60 and <60 with underlying diseases)
1st isolation to healthy <60 = (isolation to healthy <60 apply only to individuals able to isolate from vulnerable)
Change isolation to healthy <60 and (varying infected on spread day and isolation to vulnerable)
1st phase deaths fitting

2nd phase. Duration = days (ends on Holo)
2nd phase isolations, vulnerable = , healthy <60 =
2nd phase deaths fitting

3rd phase duration = days (ends on Holo) ( show 3rd phase death minimizing alternatives and one of days, time!)
3rd isolation to vulnerable =
3rd isolation to healthy <60 = (0.99 for maximun isolation, 0.00 normal life, -1.99 for coronavirus party)
Vaccination day = (Holo), uniform effectiveness. (e.g. 0.40, 1 max) ( show final for all vaccination days, time!)
Change parameters and (and show ONE individual isolating during days starting on lockdown)

D614 variant:
Spread day = Day of dominant spread (Holo), Infected on spread day (implicit) =
IFR_vul_D614 =
var IFR_non_vul_D614 =
Ro_D614 =
Do_D614 =

Display of daily deaths (n-day) moving average = (ma, e.g. 7)
Predicted results will show here

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More parameters

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Sars-cov-2 virus:
Days between Exposition to Infectiousness = (Eo, best fit 5)
Days between recovered and death = (RtoD, best fit 10)
Infectiousness duration in days = (Do, best fit 2 for nR[d]=I[d-1]/Do function) (Ro/Do is the daily reproduction number)
Normal life R in location = (Ro, estimation 3.3)
Proportion of susceptible population = (So, 1 for novel virus)
Proportion of <60 that are vulnerable = (P_vul_u60, estimation 0.0342)
Mortality for Vulnerable (IFR) = (IFR_vul, Vulnerable individuals are >60 or <60 with underlying diseases)
Mortality for Non Vulnerable (IFR) = (IFR_non_vul, Non Vulnerable individuals are healthy <60)
Average positive testing period in days = (APTP, estimation 19 days)
Proportion of asymptomatic with T cell response but without IgG response (TAK = 0.35, Sekine et al study)
Average period between recovered and losing immunity (T1 = 365, guess)
Proportion of recovered losing immunity after T1 (PRLI = 0.01, guess, same for infection and vaccine)
Proportion of <60 asymptomatic (A_u60 = 0.52, best fitting guess, if changed in Spain shows fitting results)
Proportion of >60 asymptomatic (A_o60 = 0.21, best fitting guess, if changed in Spain shows fitting results)
Average ICU hospitalizations per death = (ICU_pd, estimation 1.093)
Average ICU hospitlization duration in days = (ICU_dur, estimation 8)

Location:
Inhabitants in location= (pop, e.g. 14800000 for AMBA). Must be a conglomerate, cannot include distant locations.
Proportion of population >60 in location = (P_o60, e.g. 0.15)
Proportion of non vulnerable in location unable to isolate from vulnerables (i.e. same household) (P_non_vul_una, estimation 0.07)
ICU beds per 100.000 inhabitants in location = (ICU_beds, e.g. 26)
Day 1 of simulation (MMM DD YYYY) = (day1_str, e.g. Jan 1 2020)
Population yearly death rate for vulnerable individuals = (PYDR_vul, estimation 0.025, for >60 natural causes CDC)
Population yearly death rate for non vulnerable individuals = (PYDR_non_vul, estimation 0.001, for <60 natural causes CDC)

Other:
Days between Phase 1 and Phase 2 isolations= (e.g. 0 for abrupt isolation level change, 30 for a 30 days straight line)
Days between Phase 2 and Phase 3 isolations= (e.g. 0 for abrupt isolation level change, 30 for a 30 days straight line)

Phase 1 cross isolaton between groups = (1 - Math.sqrt(1-Iso_1_res_una) * Math.sqrt(1-Iso_1_res_abl)
Phase 2 cross isolaton between groups = (1 - Math.sqrt(1-Iso_2_res_una) * Math.sqrt(1-Iso_2_res_abl)
Phase 3 cross isolaton between groups = (1 - Math.sqrt(1-Iso_3_res_una) * Math.sqrt(1-Iso_3_res_abl)

Theoric normal life Herd Immunity Threshold (HIT) = (HIT = (1-1/Ro))
Proportion of vulnerable population in location = P_o60+(1-P_o60)*P_vul_u60 = (P_vul)
Proportion of non vulnerable population in location able to isolate from vulnerable (able population) = 1-(P_vul+P_non_vul_una) = (P_abl)

Frequently Asked Questions

To fit a metropolitan area (e.g. Madrid):

- Location name: Community of Madrid
- Total population: 6663394
- Population older than 60 years (>60): 1550571
- Date of restrictions/lockdown (estimated): March 9 2020
- Date of first death: March 3 2020
- Daily deaths (on date of deceased):
1, 0, 1, 2, 1, 10, 9, 23, 25, 38, 48, 62, 79, 109, 107, 114, 153, 209, 234, 226, 267, 281, 282, 289, 327, 273, 242, 233, 246, 263, 246, 180, 139, 185, 188, 170, 172, 119, 154, 139, 123, 140, 132, 129, 109, 107, 92, 99, 98, 99, 83, 76, 80, 64, 58, 54, 46, 55, 54, 40, 47, 41, 46, 35, 36, 41, 40, 41, 20, 29, 33, 25, 24, 19, 15, 14, 23, 18, 17, 12, 15, 21, 19, 18, 14, 7, 9, 10, 4, 9, 9, 10, 6, 8, 4, 8, 3, 5, 5, 7, 7, 4, 1, 4, 3, 4, 2, 0, 1, 1, 0, 1, 1, 1, 2, 1, 1, 2, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0

If available:
- Antibody seroprevalence date: July 5 2020
- Antibody seroprevalence for <60: 4.78%
- Antibody seroprevalence for >60: 6.03%

Please include urls to data sources. Daily deaths serie MUST be for the informed total population (e.g. the same daily deaths serie applied to London City population will produce different predictions than if applied to Greater London population).

Contact

For feedback and questions (English and Spanish): sars2seir@gmail.com

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