Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.
We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that va...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0254313 |
| _version_ | 1819023671037001728 |
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| author | Ramalingam Shanmugam Gerald Ledlow Karan P Singh |
| author_facet | Ramalingam Shanmugam Gerald Ledlow Karan P Singh |
| author_sort | Ramalingam Shanmugam |
| collection | DOAJ |
| description | We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model's ability to estimate the community's health system memory, as future policies might reduce COVID's spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable. |
| first_indexed | 2024-12-21T04:42:35Z |
| format | Article |
| id | doaj.art-668f4da27d78461aab9e999cf93b4db3 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T04:42:35Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-668f4da27d78461aab9e999cf93b4db32022-12-21T19:15:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01167e025431310.1371/journal.pone.0254313Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.Ramalingam ShanmugamGerald LedlowKaran P SinghWe present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model's ability to estimate the community's health system memory, as future policies might reduce COVID's spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.https://doi.org/10.1371/journal.pone.0254313 |
| spellingShingle | Ramalingam Shanmugam Gerald Ledlow Karan P Singh Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. PLoS ONE |
| title | Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. |
| title_full | Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. |
| title_fullStr | Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. |
| title_full_unstemmed | Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. |
| title_short | Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity. |
| title_sort | predicting covid 19 cases with unknown homogeneous or heterogeneous resistance to infectivity |
| url | https://doi.org/10.1371/journal.pone.0254313 |
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