Two spécific problems in Data Science: Demand forecasting using weather data and Non-linear causality inference

In this thesis, I investigate two specific subjects in data science, namely demand forecasting and causality inference, dividing this thesis in two main parts. The first part aims at improving demand forecasting accuracy that impacts supply chain performance. It consists of three articles aiming at studying how to enhance demand forecasting accuracy using pertinent data (e.g. operational transaction data, weather data, socio-economic data, etc.). Each article ex- plores a new statistical approach on the supply chain optimization through demand forecasting accuracy. In the first article we analyze transactional longitudinal data of several business units, matched with daily location-based weather conditions. We also study ways in which weather fluctuations affect supply chain performance though the delivery delay in days. Understanding this re- lationship is valuable both for improving sales forecast accuracy and for improving operational performance. The second article aims at explaining how weather conditions and fluc- tuations affect the accuracy of demand forecasting for seasonal products. We found that weather conditions have a significant impact on demand forecasting accuracy with reductions in percentage errors up to 45%. These results can be used to justify and motivate the integration of the impact of variability in weather in the decision making process in or- der to better anticipate demand volumes and reduce costs due to excess inventory or stock shortages. The goal of the third article is to improve demand forecasting accuracy by using the concept of spatial dependence and interpolation, and incor- porating the effects of socio-economic aspects and weather conditions in the spatial dependence structure. The accuracy of demand forecasting is improved, the reduction of the forecasting error is up to 48%. The goal of the second part is to infer the causal relationship in the case of non-linearity and heteroscedasticity. In the fourth article, a two-steps method is proposed to infer the intrinsic causal mechanism between two variables dealing with heteroscedasticity. We provide a bivariate multiplicative noise model that we extend to the multiplicative case. The two-steps Causal Hetetoscedastic Model consists of applying a causal additive model on the BAMLSS (bayesian additive model for location, scale and shape) fitted values of the estimated pa- rameters. The simulation study provides an accuracy of 0.97 on average. In this thesis, I have explored and analyzed two specific subjects in data science, which are demand forecasting and non-linear causality inference. This thesis has provided several studies improving demand forecasting accuracy by reducing the forecasting error in several contexts dealing with seasonality, through the integration of external data such as weather or socio-economic data, using complex statistical models. The causal method provided in this thesis allows the inference of inherent causal mechanism. -- Dans cette thèse j’investigue deux sujets particuliers de la science des données, à savoir la prévision de la demande et l’inférence de la causalité, divisant cette thèse en deux parties. Le but de la première partie est d’améliorer la précision de la prévision de la demande car elle impacte la performance de la chaîne logistique. Cette partie comprend trois articles dans lesquels nous étudions comment améliorer la précision des prévisions de la demande grâce à l’incorporation des données pertinentes dans le modèle d’analyse. Chacun des trois articles explore une nouvelle approche statistique. Dans le premier article, nous analysons les données transactionnelles des opérations de plusieurs unités commerciales, jumelées avec les données sur les conditions météorologiques journalières. Nous analysons aussi comment les fluctuations de la météo affectent la performance de la chaîne logistique. La compréhension de ces relations est importante et utile pour l’amélioration de la précision des prévisions de la demande. Le but du deuxième article est d’analyser et d’expliquer comment les con- ditions météorologiques ainsi que ses fluctuations impactent la précision des prévisions de la demande saisonnière. Les résultats montrent que le temps qu’il fait a un impact significatif sur cette précision, réduisant le pourcentage d’erreur de 45%. Ces résultats peuvent être utilisés pour justifier et motiver l’intégration de l’impact de la météo dans le processus décisionnel. Le troisième article utilise la dépendance spatiale pour améliorer la pré- cision des prévisions de la demande, ainsi que l’incorporation des effets des facteurs socio-économiques et des conditions météorologiques dans la structure de cette dépendance spatiale. Les résultats révèlent une amélioration de la précision et une réduction de l’erreur de prédiction allant jusqu’à 48%. La deuxième partie de cette thèse explore l’inférence de la causalité dans le cas de la non-linéarité et de l’hétéroscédasticité. Dans le quatrième article, nous proposons une méthode à deux étapes pour inférer le mécanisme causal intrinsèque entre deux variables en présence d’hétéroscédasticité. Nous proposons un modèle bivarié et mul- tiplicatif par rapport au terme d’erreur que nous étendons au cas mul- tivarié ensuite. Le modèle à deux étapes appelé Causal Heteroscedastic Model (CHM) consiste à appliquer un CAM (causal additive model) aux valeurs ajustées des paramètres estimés par un modèle BAMLSS (bayesian additive model for location, scale and shape). Les simulations effectuées montrent que le CHM trouve la bonne causalité dans 97% des cas en moyenne. Dans cette thèse, j’ai exploré et analysé deux sujets spécifiques de la science des données, qui sont la prévision de la demande et l’inférence de la causalité non-linéaire. Cette thèse comprend plusieurs études améliorant la précision des prévisions de la demande, dans différents contextes comme la saisonnalité, en réduisant l’erreur de prédiction grâce aux données pertinentes et aux outils statistiques complexes. Quant au model à deux étapes proposé, il permet l’inférence du mécanisme inhérent de la causalité

Serosurveillance after a COVID-19 Vaccine Campaign in a Swiss Police Cohort

Introduction: To assess the risk for COVID-19 of police officers, we are studying the seroprevalence in a cohort. The baseline cross-sectional investigation was performed prior to a vaccination campaign in January/February 2021, and demonstrated a seroprevalence of 12.9%. Here, we demonstrate serosurveillance results after a vaccination campaign. Methods: The cohort consists of 1022 study participants. The 3-month and 6-month follow-up visits were performed in April/May and September 2021. Data on infection and vaccination rates were obtained via measuring antibodies to the nucleocapsid protein and spike protein and online questionnaires. Results: The mean age of the population was 41 (SD 8.8) years, 72% were male and 76% had no comorbidity. Seroconversion was identified in 1.05% of the study population at the 3-month visit and in 0.73% at the 6-month visit, resulting in an infection rate of 1.8% over a time period of 6 months. In comparison, the infection rate in the general population over the same time period was higher (3.18%, P=0.018). At the 6-month visit, 77.8% of participants reported being vaccinated once and 70.5% twice; 81% had an anti-S antibody titer of >250 U/mL and 87.1% of ≥2 U/mL. No significant association between infection and job role within the department, working region, or years of experience in the job was found. Anti-spike antibody titers of vaccinated study participants showed a calculated decreasing trend 150 to 200 days after the second vaccine dose. Conclusion: These data confirm the value of the vaccination campaign in an exposed group other than healthcare professionals

Amulet or Watchman Device for Percutaneous Left Atrial Appendage Closure: Primary Results of the SWISS-APERO Randomized Clinical Trial.

Background: No study has so far compared Amulet with the new Watchman FLX in terms of residual left atrial appendage (LAA) patency or clinical outcomes in patients undergoing percutaneous LAA closure (LAAC). Methods: In the investigator-initiated SWISS APERO trial, patients undergoing LAAC were randomized (1:1) open-label to receive Amulet or Watchman 2.5 or FLX (Watchman) across 8 European centres. The primary endpoint was the composite of justified crossover to a non-randomized device during LAAC procedure or residual LAA patency detected by cardiac computed tomography angiography (CCTA) at 45 days. The secondary endpoints included procedural complications, device related thrombus (DRT), peridevice leak (PDL) at transesophageal echocardiography (TEE) and clinical outcomes at 45 days. Results: Between June 2018, and May 2021, 221 patients were randomly assigned to Amulet (111 [50.2%]) or Watchman (110 [49.8%]), of whom 25 (22.7%) patients included before October 2019 received Watchman 2.5, and 85 (77.3%) patients received Watchman FLX. The primary endpoint was assessable in 205 (92.8%) patients and occurred in 71 (67.6%) Amulet and 70 (70.0%) Watchman patients respectively (risk ratio [RR] 0.97 [95% CI 0.80- 1.16]; P=0.713). A single justified cross-over occurred in an Amulet patient who fulfilled LAA patency criteria at 45-day CCTA. Major procedure related complications occurred more frequently in the Amulet group (9.0% vs. 2.7%; P=0.047), owing to more frequent bleeding (7.2% vs.1.8%). At 45 days, the PDL rate at TEE was higher with Watchman than Amulet (27.5% vs. 13.7%, p=0.020), albeit none was major (i.e. > 5 mm), whereas DRT was detected in 1 (0.9%) patient with Amulet and 3 (3.0%) patients with Watchman at CCTA and in 2 (2.1%) and 5 (5.5%) patients at TEE, respectively. Clinical outcomes at 45 days did not differ between the groups. Conclusions: Amulet was not associated with lower rate of the composite of crossover or residual LAA patency compared with Watchman at 45-day CCTA. Amulet, was however associated with lower PDL rates at TEE, higher procedural complications and similar clinical outcomes at 45 days compared with Watchman. The clinical relevance of CCTA-detected LAA patency requires further investigation. Clinical Trial Registration: URL https://clinicaltrials.gov Unique Identifier NCT03399851

Effect of Paroxetine-Mediated G-Protein Receptor Kinase 2 Inhibition vs Placebo in Patients With Anterior Myocardial Infarction: A Randomized Clinical Trial.

Importance Left ventricular remodeling following acute myocardial infarction results in progressive myocardial dysfunction and adversely affects prognosis. Objective To investigate the efficacy of paroxetine-mediated G-protein-coupled receptor kinase 2 inhibition to mitigate adverse left ventricular remodeling in patients presenting with acute myocardial infarction. Design, Setting, and Participants This double-blind, placebo-controlled randomized clinical trial was conducted at Bern University Hospital, Bern, Switzerland. Patients with acute anterior ST-segment elevation myocardial infarction with left ventricular ejection fraction (LVEF) of 45% or less were randomly allocated to 2 study arms between October 26, 2017, and September 21, 2020. Interventions Patients in the experimental arm received 20 mg of paroxetine daily; patients in the control group received a placebo daily. Both treatments were provided for 12 weeks. Main Outcomes and Measures The primary end point was the difference in patient-level improvement of LVEF between baseline and 12 weeks as assessed by cardiac magnetic resonance tomography. Secondary end points were changes in left ventricular dimensions and late gadolinium enhancement between baseline and follow-up. Results Fifty patients (mean [SD] age, 62 [13] years; 41 men [82%]) with acute anterior myocardial infarction were randomly allocated to paroxetine or placebo, of whom 38 patients underwent cardiac magnetic resonance imaging both at baseline and 12 weeks. There was no difference in recovery of LVEF between the experimental group (mean [SD] change, 4.0% [7.0%]) and the control group (mean [SD] change, 6.3% [6.3%]; mean difference, -2.4% [95% CI, -6.8% to 2.1%]; P = .29) or changes in left ventricular end-diastolic volume (mean difference, 13.4 [95% CI, -12.3 to 39.0] mL; P = .30) and end-systolic volume (mean difference, 11.4 [95% CI, -3.6 to 26.4] mL; P = .13). Late gadolinium enhancement as a percentage of the total left ventricular mass decreased to a larger extent in the experimental group (mean [SD], -13.6% [12.9%]) compared with the control group (mean [SD], -4.5% [9.5%]; mean difference, -9.1% [95% CI, -16.6% to -1.6%]; P = .02). Conclusions and Relevance In this trial, treatment with paroxetine did not improve LVEF after myocardial infarction compared with placebo. Trial Registration ClinicalTrials.gov Identifier: NCT03274752