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Background: Palivizumab were used for the premature infant or a high-risk infant with congenital heart disease. However, recently outbreak pattern of respiratory syncytial (RS) virus infection has been varying year by year. Moreover, it also has some regional difference. Therefore, the object of the present study was to develop early detection of the timing of that outbreak had started in each prefecture. Method: We used data in National Official Sentinel Surveillance for Infectious Diseases (NOSSID). Study period was March 16th, 2011 to December 30th, 2018. We defined stating period to initiate to take palivizumab as 8 - 12 weeks before from the peak of outbreak. We estimated whether the week is included in starting period or not from April 1st to the peak of outbreak by the past number of patients of RS virus infection on week and its squared. Additionally, we have to take delay in NOSSID into consideration. Results: In nationwide, the last two seasons, the model predicted precisely the starting period. At prefectural level, the model predicted the starting period precisely in 16.6% of all year and prefectures pairs. When we consider the delay in NOSSID into consideration, the patients can start to take in 14.9% of all year and prefectures pairs. Discussion and Conclusion: The result of the probability model was not good, and thus we have to develop more sophisticated model for prediction at prefecture level.

As generally known, even though respiratory syncytial (RS) virus infection is very common infectious disease for age 0 - 1, but its vaccination for RS virus infection was not available [

Recently, the probability model for starting period to initiate to take palivizumab was examined [

Even though the proposed probability model works well in nationwide, ORCA surveillance for RS cannot be break down to prefecture level due to limitation of reporting medical facilities. It reminded as about 200 facilities, and thus the probability model cannot work at prefecture level [

Therefore, we examined some feasibility and availability of earlier prediction model for outbreak of RS virus infection by prefecture using information of NOSSID.

Study period was March 16th^{,} 2011 to December 30th, 2018. We defined stating period to initiate to take palivizumab as 8 - 12 weeks before from the peak of outbreak. We dropped the data from the peak to the end of following March. We estimated whether week t is included starting period or not from April 1st to the peak of outbreak by the number of patients of RS virus infection on week t-k (k = 0, 1, 2, …, N), and its squared [

Then we calculated probability of starting period based on the estimation results. We suppose the criterion if the estimated probability was higher than this criterion, the model predicts the starting period on the week. The criterion was defined in each prefecture as the minimum the highest probability in each season among all five seasons. If the estimated probabilities in one week were the highest, the week was identified as the model predicted starting date. If the estimated probabilities in several weeks were higher than criterion, the first date was identified as the model predicted starting date. We identified the model was precise if predicted starting date locates during t − 12 to t − 8. Also we identified that it was too early if predicted starting date was before t − 12, and too delay if predicted starting date was before t − 8.

Additionally, we have to take delay in NOSSID as described above into consideration. The information in NOSSID on week t will be available on the week t + 2. Therefore, so as to start to take the drug during t − 12 to t − 8 weeks before the peak, the model should announce it on t − 14 to t − 10 weeks before the peak.

Season for RS virus infection was defined as from 1st April to the end of March in the following year. However, the end of season in 2018/2019 season was terminated on 30th December, 2018 due to data availability.

Ethical ConsiderationsData from NOSSID was aggregated and de-linked from personal information related to patients and medical facilities: these are anonymous data. Moreover, these were published in general. Therefore, no ethical issues are posed by using these data for this study.

^{−7} (0.000) and constant was −3.28 (0.000). The number in parenthesis was p-value for each explanatory variable.

The identified criteria nationwide and by prefecture were summarized at the second column in

Criterion | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | |
---|---|---|---|---|---|---|---|

Nationwide | 0.606132 | B+ | B+ | B+ | A | H- | H- |

Hokkaido | 0.365076 | B | A | H- | B+ | H- | B |

Aomori | 0.4165 | B | B | B | B | H | H |

Iwate | 0.383186 | B | B | B | B | H- | A |

Miyagi | 0.399754 | B | H- | B | B | H | B |

Akita | 0.279561 | B | B | B | B | B | B |

Yamagata | 0.377601 | B | B | B | B | A | B |

Fukushima | 0.455593 | B | A | B | B | B+ | B |

Ibaraki | 0.508223 | H | B | B | A | H- | A |

Tochigi | 0.469719 | B | B | B | B | H- | H |

Gunma | 0.433855 | B | B | B | B | B | B |

Saitama | 0.582684 | A | B+ | H | A | A | H- |

Chiba | 0.539153 | H- | B+ | B+ | A | A | H- |

Tokyo | 0.58213 | H- | B+ | A | A | H- | H- |

Kanagawa | 0.433825 | A | B+ | B+ | A | A | H- |

Niigata | 0.379723 | A | B | A | A | H- | B |

Toyama | 0.339192 | B | B | B | B | B | B |

Ishikawa | 0.395159 | B | B | B | B | B | B |

Fukui | 0.302659 | A | B | B | B | B | B |

Yamanashi | 0.493605 | B | B | B | B | B | B |

Nagano | 0.374563 | B | B | B | B | B | H- |

Gifu | 0.248725 | B | B | B | B | A | H |

Shizuoka | 0.566482 | B+ | H | B | A | A | A |

Aichi | 0.583744 | H | B+ | H | A | A | H- |

Mie | 0.458855 | B | B | B | A | A | H |

Shiga | 0.45608 | B | B | B | B | B | B |

Kyoto | 0.35744 | B | B | B | B | A | A |

Osaka | 0.383502 | H | H- | H | A | A | H- |

Hyogo | 0.539787 | H- | B+ | B | A | B | H- |

Nara | 0.415037 | B | B | B | B | B | B |

Wakayama | 0.231956 | B | B | B | B | B | B |

Tottori | 0.450884 | B | B | B | B | B | B |

Shimane | 0.285923 | H | B | B | B | B | A |

Okayama | 0.445476 | B | B | B | B | H | A |

Hiroshima | 0.268377 | A | B | B+ | A | H | A |

Yamaguchi | 0.333154 | A | H- | B | A | H- | B+ |

Tokushima | 0.474494 | B | B | B | B | H | H- |
---|---|---|---|---|---|---|---|

Kagawa | 0.476756 | B | B | B | B | B | B |

Ehime | 0.352112 | H- | B | B | B | B | A |

Kochi | 0.253556 | B | B | B | B | B | B |

Fukuoka | 0.245601 | B | A | H- | B | B | B |

Saga | 0.217094 | B+ | B+ | B | B | B | B |

Nagasaki | 0.199576 | B | B | B | B | H | B |

Kumamoto | 0.316938 | B | B | B | H | B | B |

Oita | 0.293901 | A | B | B | B | B | H |

Miyazaki | 0.290768 | B | B | B | B | B | B |

Kagoshima | 0.381133 | H | B | B | B+ | B | B |

Okinawa | 0.343533 | B+ | B+ | B+ | A | B | H |

Note: “H” indicates that the probability model predicted starting period precisely, “B” and “A” indicates that the probability model predicted it before or after of starting period. “H-” indicates that the probability model predicted last two weeks in starting period. “B+” indicates that the probability model predicted during the two weeks before starting period. “Criterion” was defined the smallest probability among the highest probability in each season in each prefecture.

model predicted the starting period precisely. “A” and “B” indicate its prediction was before and after starting period, respectively. Moreover, when we consider the delay in NOSSID into consideration, even though the model predicted before the starting period, it predicated the starting date within two weeks before the starting period, we can start to take in the starting period. Therefore, though the model fails to predict the starting period precisely, patients can start to take within the starting period. We denote this result as “B+.” Conversely, even though the model predicted the starting period precisely, if it predicted the starting date in the last two weeks of the starting period, the patients cannot start to take within the starting period. We denote this result as “H-.” Therefore, when we consider the delay in NOSSID into consideration, the patients can start to take in the case of the estimation results was “B+” or “H” but not “H-.”

In nationwide, criterion was estimated at about 60%. The last two seasons, the model predicted precisely the starting period. However, the first three seasons, the initial date when the estimated probability was higher than criterion were before starting period. In 2016 season, the initial date when the estimated probability was higher than criterion was after starting period.

The criterions among prefectures varied from 0.1996 in Nagasaki to 0.5826 in Tokyo. In total, the model predicted precisely in 47 cases. Conversely, the initial date when the estimated probability was higher than criterion were before starting period in 195 cases. Moreover, in 46 cases, the initial date when the estimated probability was higher than criterion was after starting period.

The most precisely predicted season was 2018 and the model predicted precisely in 16 of 48 prefectures or nationwide. Conversely, the worst predicted season was 2016, the model predicted precisely only one of 48 prefectures or nationwide. There were many prefectures where the model failed to predict precisely in all six seasons.

Conversely, when we consider the delay in NOSSID into consideration, the patients in 10 prefectures can start to take in the starting period in 2014. The following year was 2013 and the next was 2015.

In total, the model predicted the starting period precisely in 16.6% of all year and prefectures pairs. When we consider the delay in NOSSID into consideration, the patients can start to take in 14.9% of all year and prefectures pairs.

The criterion which is defined as the lowest probability among the highest probability in each season was the highest in nationwide and following in Tokyo. The lowest was in Nagasaki and it was lower than 0.2. The lower criterion itself means badly fitness of the model. Moreover, the proportion of the model predicted precisely was remained about 15%. Unfortunately, the model failed to predict the starting period by prefecture in total.

The primarily reason might be that we applied the same formulation which is selected in nationwide to all prefecture, though using data and thus estimated coefficients were different for each prefecture. We might have to allow another specification in each prefecture. Moreover, though we defined the criterion as above, we have to define it more carefully with closing the situation in each prefecture. These examinations have to be our next challenge.

The secondly potential reason was shortcoming in NOSSID itself. Especially, it was weekly data, and thus it might not contain sufficient information to predict the starting period. In fact, some daily surveillance for RS virus infection such as ORCA surveillance [

We developed the prediction model for the starting period to take palivizumab. However, the results of the probability model were not good. Namely, the model predicted the starting period precisely only in 16.6%. When we consider the delay in NOSSID into consideration, the patients can start to take it only in 14.9%. Therefore, we have to develop more sophisticated model for prediction at prefecture level.

The authors declare no conflicts of interest regarding the publication of this paper.

Kurita, J., Sugawara, T., Ohkusa, Y. and Nohara, M. (2020) Application of the Probability Model for Starting Period to Initiate to Take Palivizumab by Prefecture Using National Official Sentinel Surveillance in Japan. Journal of Biosciences and Medicines, 8, 56-63. https://doi.org/10.4236/jbm.2020.81007