def METHOD_NAME(rt_f, new_cases, t_list):
    """
    Given R(t) and new_cases(t) series and a time range, find adjustment to R(t)
    so that it properly predicts the ratio of ending/starting new_cases
    """
    rt = [rt_f(t) for t in t_list]
    average_R = sum(rt) / len(rt)
    case_growth = new_cases(t_list[-1]) / new_cases(t_list[0])
    r_1_growth = math.exp((average_R - 1) * (len(t_list) - 1) / InferRtConstants.SERIAL_PERIOD)
    growth_ratio = case_growth / r_1_growth
    if growth_ratio > 0:
        dr = math.log(growth_ratio) * InferRtConstants.SERIAL_PERIOD / (len(t_list) - 1)
    else:  # something funny so skip
        dr = 0.0
    adj_r_f = lambda t: rt_f(t) + dr
    return (average_R, growth_ratio, adj_r_f)