A bivariate prediction approach for adapting the health care system response to the spread of COVID-19

Berta, Paolo; Paruolo, Paolo; Verzillo, Stefano; Lovaglio, Pietro Giorgio

doi:10.1371/journal.pone.0240150

Cited by 13 publications

(8 citation statements)

References 25 publications

(31 reference statements)

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“…However, a second wave has subsequently invested the whole country determining high rates of infection and, consequently, mortality [ 1 ]. Waiting for a relevant reduction in the transmission of the disease thanks to the mass vaccination campaigns, we are still witnessing the stress on health structures for a period to come still uncertain [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

A seven week observational analysis of clinical activities in a North Italian orthopaedic hospital during the second wave of SARS-CoV-2 pandemic: far from usual volumes, but different from the first wave

Zagra

Faraldi

Andreata

et al. 2021

International Orthopaedics (SICOT)

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Purpose We previously described the radical changes occurred in an orthopaedic hospital in Milan (Italy) during the first SARS-CoV-2 pandemic outbreak. Currently, during the second wave, the situation is still far from normality. Here we describe the changes that took place, and are still ongoing, in the clinical practice. Methods Number and type of admissions, outpatients activity, ER and urgent procedures in SARS-CoV-2 negative and positive patients have been analyzed over seven weeks (October 26th–December 13th, 2020) and compared with the correspondent period in 2019 and the same timeframe during the first wave (February 24th–April 10th). Results 2019 vs. 2020: Overall admissions decreased by 39.8%; however, while admissions for elective surgery dropped by 42.0%, urgent surgeries increased by 117.0%. Rehabilitation admissions declined by 85.2%. White and green priority ER consultations declined by 41.6% and 52.0%, respectively; yellow and red increased by 766.7% and 400.0%, respectively. Second vs. first wave: Overall admissions increased by 58.6% with a smoother decrement in weekly admissions than during the first wave. Disparity of acute admissions vs. rehabilitation expanded: Acute cases increased by 63.6% while rehabilitation cases decreased by 8.7%. Admissions to triage procedures increased by 72.3%. Conclusions Activity levels are far from normality during the second COVID-19 wave. Elective surgery and outpatients-related activities are still strongly limited compared to 2019 while the number of urgent cases treated increased consistently. SARS-CoV-2 positive emergencies are slightly higher than during the first wave. These important changes are expected to impact on health service and hospital budget for long.

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Section: Introductionmentioning

confidence: 99%

A seven week observational analysis of clinical activities in a North Italian orthopaedic hospital during the second wave of SARS-CoV-2 pandemic: far from usual volumes, but different from the first wave

Zagra

Faraldi

Andreata

et al. 2021

International Orthopaedics (SICOT)

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“…In the model, we also saw that past incidence changes at multiple lags have statistically significant effects on census. While previous studies have focused on other types of leading indicators [12,13], our model results and our observations demonstrate that local infection incidence can be a very effective leading indicator for COVID-19 hospital census.…”

Section: Principal Resultsmentioning

confidence: 43%

“…Recently, VECM has been used to forecast the demand for intensive care units during the COVID-19 pandemic by including hospital admission as a leading indicator [ 12 ]. Although hospital admission is a natural choice as a leading indicator, it has a short period of lead time (ie, hours to days) and thus, limited predictive power.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

Nguyen¹,

Turk²,

McWilliams³

2021

JMIR Public Health Surveill

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Background COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. Objective The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. Methods The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. Results The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. Conclusions When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

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“…Pada 31 Desember 2019, Kantor World Health Organization (WHO) yang berlokasi di China melaporkan kasus pneumonia yang belum diketahui penyebabnya di Kota Wuhan, Provinsi Hubei, China dan kemudian pada 7 Januari 2020 China mengidentifikasi kasus tersebut sebagai kasus coronavirus . Kurang dari satu bulan, coronavirus dari manusia ke manusia telah menyebar ke berbagai negara di dunia (Berta et al, 2020). WHO (2020), pada 30 Januari 2020 menetapkan Coronavirus Disease 2019 (COVID-19) sebagai pandemi .…”

Section: Pendahuluanunclassified

Analisis Spasial Kasus COVID-19 sampai dengan PPKM Jilid Dua

Rahadiyan¹,

Pasaribu²

2021

semnasoffstat

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Pandemi COVID-19 merupakan penyebaran wabah penyakit menular COVID-19 yang diakibatkan oleh infeksi SARS-CoV-2 di semua negara termasuk Indonesia. Pada 31 Maret 2020, diterbitkan Peraturan Pemerintah Nomor 21 Tahun 2020 tentang pembatasan sosial berskala besar untuk membatasi mobilitas orang dan barang. Pemerintah daerah menetapkan kebijakan karantina di beberapa wilayah di Pulau Jawa dan Bali berdasarkan Instruksi Mendagri Nomor 1 Tahun 2021 tentang Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) Jilid 1 dan Jilid 2. Penelitian ini bertujuan untuk mengetahui gambaran umum, mengidentifikasi keragaman spasial, dan menganalisis faktor-faktor yang memengaruhi karakteristik spasial kasus COVID-19 di Pulau Jawa dan Bali sampai dengan masa PPKM Jilid 2 dengan Geographically Weighted Negative Binomial Regression. Hasil analisis menunjukkan terdapat keragaman spasial kasus COVID-19 di Pulau Jawa dan Bali. Kepadatan penduduk berpengaruh signifikan di seluruh kabupaten/kota, sedangkan rasio tenaga kesehatan per 1000 penduduk, indeks kualitas udara, dan pengeluaran kesehatan berpengaruh signifikan di beberapa kabupaten/kota di Pulau Jawa dan Bali.

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A bivariate prediction approach for adapting the health care system response to the spread of COVID-19

Cited by 13 publications

References 25 publications

A seven week observational analysis of clinical activities in a North Italian orthopaedic hospital during the second wave of SARS-CoV-2 pandemic: far from usual volumes, but different from the first wave

A seven week observational analysis of clinical activities in a North Italian orthopaedic hospital during the second wave of SARS-CoV-2 pandemic: far from usual volumes, but different from the first wave

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence

Analisis Spasial Kasus COVID-19 sampai dengan PPKM Jilid Dua

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