<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Effect of Migrant Labourer Inflow on the Early Spread of Covid-19 in Odisha: A Case Study."
Abstract:"Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early stage of national lockdown (March June2020) due to the Covid-19 outbreak as their places of work became Covid hotspots while Odisha was much less affected. This triggered the Odisha government to take precautionary measures such as mandatory quarantine of returning migrants, setting up of containment zones, and establishing temporary medical centres (TMC). Moreover, it was necessary for the government to devise a policy that could slow down the spread of Covid-19 in Odisha due to inflow of migrants. Being part of a task-force constituted by government to understand Covid-19 spread dynamics in Odisha, we predicted the number of people who would get infected primarily due to reverse-migration. This helped the government to make timely resource mobilisation. After analyzing reasons behind the rise in infections at various districts with large migrant population, we mapped the prediction problem to Sequential Probability Ratio Test (SPRT) of Abraham Wald. Our predictions were highly accurate when compared with real data that were obtained at a later stage. Two levels of SPRT were carried out over the data provided by the government. Use of SPRT for Covid-19 spread analysis is novel, particularly to predict the number of possible infections much ahead in time due to the sudden inflow of migrants.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [3];
Q3 = [];
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Q5 = [];
Q6 = [1].
Effect of Migrant Labourer Inflow on the Early Spread of Covid-19 in Odisha: A Case Study
Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early stage of national lockdown (March June2020) due to the Covid-19 outbreak as their places of work became Covid hotspots while Odisha was much less affected. This triggered the Odisha government to take precautionary measures such as mandatory quarantine of returning migrants, setting up of containment zones, and establishing temporary medical centres (TMC). Moreover, it was necessary for the government to devise a policy that could slow down the spread of Covid-19 in Odisha due to inflow of migrants. Being part of a task-force constituted by government to understand Covid-19 spread dynamics in Odisha, we predicted the number of people who would get infected primarily due to reverse-migration. This helped the government to make timely resource mobilisation. After analyzing reasons behind the rise in infections at various districts with large migrant population, we mapped the prediction problem to Sequential Probability Ratio Test (SPRT) of Abraham Wald. Our predictions were highly accurate when compared with real data that were obtained at a later stage. Two levels of SPRT were carried out over the data provided by the government. Use of SPRT for Covid-19 spread analysis is novel, particularly to predict the number of possible infections much ahead in time due to the sudden inflow of migrants.