Latest Statewide COVID-19 Reproductive Number Estimates and Projections for Iowa
Grant Brown
Using report compiled on: 2020-05-29
Introduction
This automatically generated report captures the changes in one of our approaches to COVID modelling in Iowa. The team at the University of Iowa has provided a number of white-papers to the Iowa Department of Public Health, including:
- An assessment of the IHME model for IDPH: (link)
- Initia, Iowa-specific models, submitted April 20, 2020: (link)
- Clarifications of appropriate use of symptom onset data, submitted April 27, 2020 (link)
- Update on Iowa-specific model M2, submitted May 04, 2020: (link)
This specific report concerns model M2 from the April 20 and May 04 whitepapers, and is constructed using entirely public information. Given the sensitivity of these models to prior assumptions, and the data used, results may not preciesly concord with models used for other purposes or constructed under different conditions. The information presented here is intended to supplement, not replace, official guidance on COVID-19 from reputable public health organizations:
We’re also not the only group producing Iowa-specific forecasts. You may also be interested in the following projects:
Reproductive Numbers
Interventions to control infectious diseases strive to drive the “reproductive number” below 1. While this quantity comes in many forms, and is derived in many ways for different models, in general it captures the number of secondary infections expected per infectious individual. When this number falls below 1, the spread of the disease is expected to slow and stop, while values greater than 1 indicate that the disease is likely to continue to spread exponentially until some other phenomenon interrupts transmission (additional interventions, natural behavioral changes, a lack of susceptible individuals).
The plot below provides the latest plot estimating our relationship to this threshold. Namely, it estimates the probability that the reproductive number has fallen below 1 at any point during the outbreak in Iowa, and provides a forecast
Mortality Projections
The models used here are trained on publicly available mortality data - as such, predicting mortality is one of their principal uses. In this document, we present the latest statewide projections, as well as a comparison to the official May-02 report to see how the predictions have evolved over time (where appropriate).
Latest Results
These results are based on the most recently available models (generally 1-2 days out of date)
Latest EA-RN information for Iowa
Latest Projected Mortality
| Date | Observed Mortality) | Predicted Mortality |
|---|---|---|
| 2020-03-08 | 0 | 0 (0, 0) |
| 2020-03-09 | 0 | 0 (0, 0) |
| 2020-03-10 | 0 | 0 (0, 0) |
| 2020-03-11 | 0 | 0 (0, 0) |
| 2020-03-12 | 0 | 0 (0, 0) |
| 2020-03-13 | 0 | 0 (0, 1) |
| 2020-03-14 | 0 | 0 (0, 1) |
| 2020-03-15 | 0 | 0 (0, 1) |
| 2020-03-16 | 0 | 1 (0, 2) |
| 2020-03-17 | 0 | 1 (0, 3) |
| 2020-03-18 | 0 | 1 (0, 4) |
| 2020-03-19 | 0 | 2 (1, 5) |
| 2020-03-20 | 0 | 2 (1, 6) |
| 2020-03-21 | 0 | 3 (1, 7) |
| 2020-03-22 | 0 | 4 (1, 9) |
| 2020-03-23 | 0 | 4 (2, 11) |
| 2020-03-24 | 1 | 5 (2, 13) |
| 2020-03-25 | 1 | 6 (2, 15) |
| 2020-03-26 | 1 | 7 (3, 18) |
| 2020-03-27 | 3 | 8 (3, 20) |
| 2020-03-28 | 3 | 10 (4, 23) |
| 2020-03-29 | 4 | 11 (5, 25) |
| 2020-03-30 | 6 | 13 (5, 28) |
| 2020-03-31 | 7 | 15 (6, 31) |
| 2020-04-01 | 9 | 16 (7, 34) |
| 2020-04-02 | 11 | 18 (8, 37) |
| 2020-04-03 | 11 | 20 (9, 41) |
| 2020-04-04 | 11 | 22 (10, 44) |
| 2020-04-05 | 22 | 24 (12, 48) |
| 2020-04-06 | 25 | 27 (13, 52) |
| 2020-04-07 | 26 | 29 (14, 56) |
| 2020-04-08 | 27 | 32 (16, 60) |
| 2020-04-09 | 27 | 35 (17, 64) |
| 2020-04-10 | 31 | 38 (19, 69) |
| 2020-04-11 | 34 | 42 (21, 74) |
| 2020-04-12 | 41 | 45 (24, 80) |
| 2020-04-13 | 44 | 48 (26, 85) |
| 2020-04-14 | 44 | 52 (28, 91) |
| 2020-04-15 | 53 | 56 (31, 99) |
| 2020-04-16 | 60 | 60 (34, 105) |
| 2020-04-17 | 64 | 64 (37, 111) |
| 2020-04-18 | 74 | 68 (40, 118) |
| 2020-04-19 | 75 | 73 (43, 125) |
| 2020-04-20 | 79 | 77 (47, 132) |
| 2020-04-21 | 83 | 82 (50, 139) |
| 2020-04-22 | 90 | 88 (55, 148) |
| 2020-04-23 | 96 | 93 (58, 155) |
| 2020-04-24 | 107 | 99 (61, 164) |
| 2020-04-25 | 112 | 107 (66, 173) |
| 2020-04-26 | 118 | 114 (69, 181) |
| 2020-04-27 | 127 | 123 (75, 190) |
| 2020-04-28 | 136 | 128 (80, 199) |
| 2020-04-29 | 148 | 136 (86, 209) |
| 2020-04-30 | 162 | 144 (92, 218) |
| 2020-05-01 | 170 | 152 (99, 226) |
| 2020-05-02 | 175 | 162 (105, 237) |
| 2020-05-03 | 184 | 169 (110, 247) |
| 2020-05-04 | 188 | 179 (116, 256) |
| 2020-05-05 | 207 | 187 (124, 266) |
| 2020-05-06 | 219 | 197 (132, 277) |
| 2020-05-07 | 231 | 206 (141, 288) |
| 2020-05-08 | 243 | 217 (150, 299) |
| 2020-05-09 | 252 | 227 (160, 314) |
| 2020-05-10 | 265 | 238 (170, 328) |
| 2020-05-11 | 271 | 249 (179, 343) |
| 2020-05-12 | 289 | 260 (189, 354) |
| 2020-05-13 | 306 | 272 (202, 365) |
| 2020-05-14 | 318 | 282 (211, 379) |
| 2020-05-15 | 336 | 294 (220, 391) |
| 2020-05-16 | 346 | 305 (230, 404) |
| 2020-05-17 | 351 | 317 (241, 421) |
| 2020-05-18 | 355 | 329 (252, 439) |
| 2020-05-19 | 367 | 341 (263, 456) |
| 2020-05-20 | 400 | 354 (278, 469) |
| 2020-05-21 | 414 | 368 (292, 482) |
| 2020-05-22 | 441 | 383 (306, 495) |
| 2020-05-23 | 446 | 399 (319, 513) |
| 2020-05-24 | 456 | 413 (329, 527) |
| 2020-05-25 | 460 | 425 (341, 539) |
| 2020-05-26 | 480 | 443 (353, 558) |
| 2020-05-27 | 496 | 457 (366, 572) |
| 2020-05-28 | 510 | 469 (378, 584) |
| 2020-05-29 | NA | 485 (389, 598) |
| 2020-05-30 | NA | 496 (401, 612) |
| 2020-05-31 | NA | 507 (409, 628) |
| 2020-06-01 | NA | 522 (421, 644) |
| 2020-06-02 | NA | 536 (433, 666) |
| 2020-06-03 | NA | 552 (441, 684) |
| 2020-06-04 | NA | 561 (449, 698) |
| 2020-06-05 | NA | 581 (456, 722) |
| 2020-06-06 | NA | 597 (466, 733) |
| 2020-06-07 | NA | 613 (473, 749) |
| 2020-06-08 | NA | 627 (479, 767) |
| 2020-06-09 | NA | 639 (486, 798) |
| 2020-06-10 | NA | 652 (493, 821) |
| 2020-06-11 | NA | 664 (500, 859) |
| 2020-06-12 | NA | 680 (506, 893) |
| 2020-06-13 | NA | 692 (512, 932) |
| 2020-06-14 | NA | 709 (519, 972) |
| 2020-06-15 | NA | 730 (526, 1012) |
| 2020-06-16 | NA | 742 (529, 1055) |
| 2020-06-17 | NA | 755 (531, 1097) |
| 2020-06-18 | NA | 772 (534, 1128) |
| 2020-06-19 | NA | 791 (538, 1161) |
| 2020-06-20 | NA | 810 (545, 1208) |
| 2020-06-21 | NA | 826 (551, 1256) |
| 2020-06-22 | NA | 842 (558, 1315) |
| 2020-06-23 | NA | 855 (563, 1404) |
| 2020-06-24 | NA | 864 (568, 1485) |
| 2020-06-25 | NA | 876 (572, 1554) |
| 2020-06-26 | NA | 889 (577, 1633) |
| 2020-06-27 | NA | 902 (582, 1754) |
| 2020-06-28 | NA | 913 (586, 1860) |
Comparison to May 02 Report
These results are reproduced from the models included in the May 02 report using model M2. The mortality projections are compared to the subsequently reported counts.
Reproductive Number Projections
Mortality Projections
| Date | Observed Mortality) | Predicted Mortality |
|---|---|---|
| 2020-03-08 | 0 | 0 (0, 0) |
| 2020-03-09 | 0 | 0 (0, 0) |
| 2020-03-10 | 0 | 0 (0, 0) |
| 2020-03-11 | 0 | 0 (0, 0) |
| 2020-03-12 | 0 | 0 (0, 0) |
| 2020-03-13 | 0 | 0 (0, 0) |
| 2020-03-14 | 0 | 0 (0, 1) |
| 2020-03-15 | 0 | 0 (0, 1) |
| 2020-03-16 | 0 | 0 (0, 1) |
| 2020-03-17 | 0 | 1 (0, 2) |
| 2020-03-18 | 0 | 1 (0, 3) |
| 2020-03-19 | 0 | 1 (0, 4) |
| 2020-03-20 | 0 | 2 (1, 4) |
| 2020-03-21 | 0 | 2 (1, 5) |
| 2020-03-22 | 0 | 3 (1, 6) |
| 2020-03-23 | 0 | 3 (1, 7) |
| 2020-03-24 | 1 | 4 (2, 9) |
| 2020-03-25 | 1 | 5 (2, 10) |
| 2020-03-26 | 1 | 6 (2, 12) |
| 2020-03-27 | 3 | 7 (3, 13) |
| 2020-03-28 | 3 | 8 (3, 15) |
| 2020-03-29 | 4 | 9 (4, 17) |
| 2020-03-30 | 6 | 10 (5, 19) |
| 2020-03-31 | 7 | 11 (5, 21) |
| 2020-04-01 | 9 | 13 (6, 22) |
| 2020-04-02 | 11 | 14 (7, 25) |
| 2020-04-03 | 11 | 16 (8, 27) |
| 2020-04-04 | 11 | 18 (9, 29) |
| 2020-04-05 | 22 | 20 (10, 32) |
| 2020-04-06 | 25 | 21 (11, 35) |
| 2020-04-07 | 26 | 23 (13, 37) |
| 2020-04-08 | 27 | 26 (14, 40) |
| 2020-04-09 | 27 | 28 (16, 44) |
| 2020-04-10 | 31 | 30 (17, 47) |
| 2020-04-11 | 34 | 33 (19, 50) |
| 2020-04-12 | 41 | 36 (21, 54) |
| 2020-04-13 | 44 | 40 (23, 59) |
| 2020-04-14 | 44 | 43 (26, 64) |
| 2020-04-15 | 53 | 47 (29, 69) |
| 2020-04-16 | 60 | 50 (32, 74) |
| 2020-04-17 | 64 | 54 (36, 79) |
| 2020-04-18 | 74 | 59 (39, 85) |
| 2020-04-19 | 75 | 64 (42, 91) |
| 2020-04-20 | 79 | 69 (46, 97) |
| 2020-04-21 | 83 | 74 (52, 104) |
| 2020-04-22 | 90 | 79 (57, 110) |
| 2020-04-23 | 96 | 85 (63, 117) |
| 2020-04-24 | 107 | 92 (68, 126) |
| 2020-04-25 | 112 | 99 (75, 134) |
| 2020-04-26 | 118 | 107 (80, 142) |
| 2020-04-27 | 127 | 114 (87, 151) |
| 2020-04-28 | 136 | 122 (96, 160) |
| 2020-04-29 | 148 | 132 (103, 172) |
| 2020-04-30 | 162 | 143 (110, 182) |
| 2020-05-01 | 170 | 154 (120, 195) |
| 2020-05-02 | 175* | 165 (130, 210) |
| 2020-05-03 | 184* | 177 (142, 225) |
| 2020-05-04 | 188* | 189 (151, 243) |
| 2020-05-05 | 207* | 203 (162, 261) |
| 2020-05-06 | 219* | 215 (175, 280) |
| 2020-05-07 | 231* | 232 (183, 302) |
| 2020-05-08 | 243* | 246 (194, 332) |
| 2020-05-09 | 252* | 263 (205, 363) |
| 2020-05-10 | 265* | 281 (215, 396) |
| 2020-05-11 | 271* | 297 (223, 430) |
| 2020-05-12 | 289* | 319 (234, 469) |
| 2020-05-13 | 306* | 338 (244, 515) |
| 2020-05-14 | 318* | 359 (255, 559) |
| 2020-05-15 | 336* | 387 (266, 610) |
| 2020-05-16 | 346* | 417 (277, 666) |
| 2020-05-17 | 351* | 439 (290, 730) |
| 2020-05-18 | 355* | 462 (303, 804) |
| 2020-05-19 | 367* | 492 (313, 895) |
| 2020-05-20 | 400* | 530 (324, 1012) |
| 2020-05-21 | 414* | 566 (336, 1142) |
| 2020-05-22 | 441* | 598 (352, 1301) |
| 2020-05-23 | 446* | 644 (367, 1446) |
| 2020-05-24 | 456* | 683 (383, 1590) |
| 2020-05-25 | 460* | 723 (394, 1748) |
| 2020-05-26 | 480* | 769 (405, 1923) |
| 2020-05-27 | 496* | 818 (417, 2147) |
| 2020-05-28 | 510* | 865 (429, 2371) |
| 2020-05-29 | NA | 919 (442, 2601) |
| 2020-05-30 | NA | 971 (455, 2828) |
| 2020-05-31 | NA | 1030 (464, 3095) |
| 2020-06-01 | NA | 1115 (472, 3403) |