{"doi":"10.1101/2024.01.13.24301248","title":"Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States","abstract":"The novel coronavirus (COVID-19) pandemic, first identified in Wuhan China in December 2019, has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. There have been more than half a billion human infections and more than 6 million deaths globally attributable to COVID-19. Although treatments and vaccines to protect against COVID-19 are now available, people continue being hospitalized and dying due to COVID-19 infections. Real-time surveillance of population-level infections, hospitalizations, and deaths has helped public health officials better allocate healthcare resources and deploy mitigation strategies. However, producing reliable, real-time, short-term disease activity forecasts (one or two weeks into the future) remains a practical challenge. The recent emergence of robust time-series forecasting methodologies based on deep learning approaches has led to clear improvements in multiple research fields. We propose a recurrent neural network model named Fine-Grained Infection Forecast Network (FIGI-Net), which utilizes a stacked bidirectional LSTM structure designed to leverage fine-grained county-level data, to produce daily forecasts of COVID-19 infection trends up to two weeks in advance. We show that FIGI-Net improves existing COVID-19 forecasting approaches and delivers accurate county-level COVID-19 disease estimates. Specifically, FIGI-Net is capable of anticipating upcoming sudden changes in disease trends such as the onset of a new outbreak or the peak of an ongoing outbreak, a skill that multiple existing state-of-the-art models fail to achieve. This improved performance is observed across locations and periods. Our enhanced forecasting methodologies may help protect human populations against future disease outbreaks.","journal":"medRxiv","year":2024,"id":486569,"datarank":0.22983716637205437,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.02189301220407076,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.02189301220407076,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":3,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9444,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":300525,"name":"Leonardo Clemente","orcid":null,"position":1,"is_corresponding":false},{"id":696829,"name":"Xiang Pan","orcid":"0000-0002-7521-0702","position":2,"is_corresponding":false},{"id":696827,"name":"Junbong Jang","orcid":"0000-0001-9317-6520","position":3,"is_corresponding":false},{"id":105567,"name":"Mauricio Santillana","orcid":"0000-0002-4206-418X","position":4,"is_corresponding":false},{"id":573590,"name":"Kwonmoo Lee","orcid":"0000-0001-6838-7094","position":5,"is_corresponding":false},{"id":1330396,"name":"Tzu‐Hsi Song","orcid":"0000-0002-3670-8970","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:01.404471Z","pmid":"38293076","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}